LUNG CANCER Searching early biomarkers in blood

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1 LUNG CANCER Searching early biomarkers in blood Eloisa Jantus Lewintre Laboratorio Oncología Molecular- FIHGUV Servicio Oncología Médica, CHGUV Dpto Biotecnología- Universitat Politècnica de València CIBERONC, Respiratory tract tumour programme

2 Early biomarkers in different scenarios Screening & Diagnosis Early diagnosis Is cancer present? Early detection of molecular alterations Is any druggable alteration present? Early detection of relapse After tumor resection Early detection of relapse *Dr. Paz-Ares Early detection of resistance Monitoring: Is it possible to early detect resistance mechanism?

3 1º SCENARIO Screening & Diagnosis Is lung cancer present?

4 1º SCENARIO CHALLENGES Enormous complexity and variability of cancer The lower frequency and volume of aberrations Potentially confounding phenomena such as clonal expansions of non-tumorous tissues The accumulation of cancer-associated mutations with age The incomplete insight into driver alterations. Hence, pre-knowledge about endangered organs significantly extends options for analysis which significantly facilitate early detection efforts. Heitzer E. et al, Precision Oncology, 2017

5 1º SCENARIO Population at risk Surveillance General population Screening Known organ at risk Chronic exposure to toxic agents No knowledge of organ at risk Systemic & Proximal samples (i.e. sputum, saliva, BAL)

6 GENOME 1º SCENARIO TRACER-X Data based on 100 NSCLC patients -> profiling preoperative plasma samples and compared to tissue samples Exome seq in Tumor samples Blood: multiplex PCR for patient s specific SNVs Abbosh, Nature 2017

7 1º SCENARIO GENOME Circulating Cell- Free Genome Atlas (CCGA) clinicaltrials.gov: NCT GRAIL company (investment $900 millions) & Memorial Sloane Kettering N=7.000 (cancer ) (no-cancer) To address two main challenges 1. sensitivity for early stage disease 2. the need for exquisite specificity 508 genes analyzed Preliminary data : N= 161 (Breast, lung and prostate pts In 89 % of pts at least one mutation detected (tissue and blood) -> 85 % in those with lung cancer A total of 76% of actionable mutations detected in tumor tissue were also detected in cfdna Aravanis A., Cell 2017 Razavi et al, ASCO 2017

8 1º SCENARIO TRANSCRIPTOMICS: mrna, mirna, lncrna RNA isolation Cui et al, Lung Cancer 2018

9 1º SCENARIO TRANSCRIPTOMICS: mirna 24 mirnas signature -> MILD trial -> N= 1000 controls ; N= 85 LC (Sozzi G, et al ; J Clin Oncol 2014) Sensitiviy: 87% Specificity: 81% mir- Test -> High-risk individuals (n = 1115) enrolled in the Continuous Observation of Smoking Subjects (COSMOS) lung cancer screening program. (Montani et al; J Natl Cancer Inst 2015) Sensitivity of 79.2% (95% CI = 67.7% to 90.7%) Specificty of 75.9% (95% CI = 73.3% to 78.5%) Tumor derived exosomal mirnas for early detection of lung cancer (let 7b, mir24, mir486 and let 7e) (N=48 training set and N=50 validation set) (Jin et al, Clin Cancer Res 2017)

10 1º SCENARIO PROTEOMIC, EPIGENOMIC C4d Four genes signature Sample: BAL Sample: BAL Ajona D, J Natl Cancer Inst 2013 Diaz-Lagares A, Clin Cancer Res 2016.

11 METABOLOMICS Fundación Mutua Madrileña APM-10/15 (Generalitat Valenciana) 1º SCENARIO Validation cohort NSCLC= 40 Healthy controls= 13 BPD=27 Training cohort NSCLC= 142 Healthy controls= 74 Puchades et al, Oncotarget 2016

12 Louis E,. J Thorac Oncol º SCENARIO METABOLOMICS Plasma Training set NSCLC= 233 Healthy controls= 226 Validation set NSCLC= 98 Healthy controls= 89 NSCLC correctly classified: 75% Healthy Controls correctly classified: 82%

13 GENOME & PROTEINS 1º SCENARIO CancerSEEK: circulating proteins and mutations in cell-free DNA Capacity to identify the presence of relatively early cancers Able to localize the organ of origin of these cancers. Patients = 1,005 (non-metastatic cancers of the ovary, liver, stomach, pancreas, esophagus, colorectum, lung, or breast) Controls = 802 PCR-based assay 16 genes 61-amplicon panel 33 bp/ amplicon Gene AKT1 APC BRAF CDKN2A CTNNB1 EGFR FBXW7 FGFR2 GNAS HRAS KRAS NRAS PIK3CA PPP2R1A PTEN TP53 PROTEIN CA-125 CA19-9 CEA HGF Myeloperoxidase OPN Prolactin TIMP-1 AFP Angiopoietin-2 AXL CA 15-3 CD44 CYFRA 21-1 DKK1 Endoglin FGF2 Follistatin Galectin-3 G-CSF GDF15 HE4 IL-6 IL-8 Kallikrein-6 Leptin Mesothelin Midkine NSE OPG PAR segfr sfas SHBG sher2/segfr2/serbb2 specam-1 TGFa Thrombospondin-2 TIMP-2 Multiplex assay (Luminex) 39 proteins (for organ assessment) 8 proteins (including in Cancer SEEK) Cohen et al., Science 2018

14 GENOME & PROTEINS CancerSEEK 1º SCENARIO Positive case Presence of a mutation in one assayed gene OR, Elevation in the level of one of the analyzed proteins. Sensitivity: 70% Specificity: 99% Cohen et al., Science 2018

15 GENOME & PROTEINS CancerSEEK 1º SCENARIO 58% 42% Estimated cost/ sample: < 500 USD Lung cancer = 38% Cohen et al., Science 2018

16 2º SCENARIO Early detection of molecular alterations Is any druggable alteration present? * Dr. Costa

17 GENOME 2º SCENARIO Blood based approaches for de novo discovery of actionable targets in patients with cancer. Siravegna, G. et al. (2017) Nat. Rev. Clin. Oncol.

18 2º SCENARIO To analyze the clinical utility of plasma-based targeted NGS using cell-free circulating tumor DNA (ctdna) for advanced-stage lung ADC patients, as a complement or alternative to tissue-based molecular profiling 12 Hospitals 3 cohorts (advanced lung ADC) COHORT 1 N=69 Insufficient tissue (for EGFR, ALK or ROS1 analysis) NGS SNVs= in 73 genes Gene Copy Nr: 18 genes Fusions/rearrangements: 6 genes Indels: 23 genes This panel was designed to report on gene alterations with current clinical utility Gene variant actionability was stratified into four levels according to the OncoKB criteria (Jordan et al. Cancer Discov 2017) Patients with > 2 level 1-4 oncogenic drivers were grouped with the highest-level actionable driver. Garrido P et al, WCLC 2017

19 2º SCENARIO Patients included : N= 156 Characteristics Global Cohort 1 Total (58.5%) Gender - Female - Male Smoking history - Never smoker - Former smoker - Current smoker Performance status Stage - IIIB - IV M1a - IV M1b Nº metastatic organs > 3 Nº of prior lines of therapy (56%) 52 (44%) 50 (42%) 46 (39%) 22 (19%) 47 (40%) 58 (49%) 12 (10%) 1 (1%) 1 (1%) 42 (36%) 75 (63%) 101(86%) 18 (14%) 39 (33%) 47 (40%) 20 (17%) 12 (17%) (1.5%) 27 (39,5%) 41 (59%) (55.5%) 21 (30%) 9 (13%) 1 (1.5%) Level alteration Global Cohort 1 Level 1 17 (14 %) 6 (9 %) Level 2-2A -2B 5 (4 %) 1 (< 1 %) 1 (1 %) 0 Level 3 10 (8 %) 9 (13 %) Level 4 24 (20 %) 21 (30 %) N of patients with potentially actionable alterations 57 (48 %) 37 (53 %) Total of patients Garrido et al, WCLC 2017

20 3º SCENARIO Early detection of resistance mechanisms Monitoring: Is it possible to early detect resistance mechanism?

21 GENOME 3º SCENARIO Blood based approaches for dynamic monitoring of targets -> requieres a priori knowledge of resistance mechanisms. Siravegna, G. et al. (2017) Nat. Rev. Clin. Oncol.

22 Modified from Mok, IASLC 2017 GENOME EGFR mut (+) 3º SCENARIO

23 3º SCENARIO EGFR mut (+): monitoring treatment 6 weeks Thress, ASCO 2017

24 EGFR mut (+): early detection of resistance 3º SCENARIO AURA 1 NEW PARADIGM: Data support that plasma genotyping as a screening test for T790M prior to performing an EGFR resistance biopsy Oxnard et al, J Clin Oncol 2016

25 %mutant allele Mutant Fraction CASE 1: EGFR mut + 3º SCENARIO EGFR mut (+): early detection of resistance but using sensitive methods wk qpcr Del.747_750A Beaming Del.747_750A Beaming Del.747_750A Beaming T790M qpcr Del.747_750A qpcr T790M 1st generation EGFR-TKI 3rd generation EGFR-TKI

26 Finally. 1st scenario Screening (high risk polulation) Complementary to other biomarkers. Better results in combined approaches 2nd scenario Early stages NGS: Improving results and lower costs -> in a near future a complementary and/or alternative? to tissue biopsies Advanced stages Diagnosis 3rd scenario Follow up Progression Targeted therapy 1 Targeted therapy 2 Molecular alteration detected If Initial biopsy insufficient, unavailable, or undergenotyped Molecular alteration detected 1st option 2nd option quantification If negative quantification Jantus-Lewintre, 2018

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