Liquid biopsy to monitor genetic alterations in plasma as non-invasive pharmacogenetic-based approach to evaluate predictive biomarkers during treatment in cancer patients.

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PhD Course “Clinical and Translational Science”

Director: Prof. Stefano Del Prato

Doctorate Thesis

Liquid biopsy to monitor genetic alterations in plasma as

non-invasive pharmacogenetic-based approach to evaluate

predictive biomarkers during treatment in cancer patients

Candidate Tutors

Dr Eleonora Rofi

Prof. Romano Danesi

Dr Marzia Del Re

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A te mio piccolo Tommaso.

“Lo studio e la ricerca della verità e della bellezza rappresentano una sfera

di attività in cui è permesso di rimanere bambini per tutta la vita.”

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Pharmacogenetics and Precision Medicine in Oncology

Genetic variants of target genes involved in drug metabolism, transport or molecular targets/pathways and changes in their expression levels can affect the individual differences in drug reactivity by affecting the in vivo concentration and sensitivity of the drug. The study of how a subject's unique genetic makeup influences both the variability in responses to drugs and their adverse events is the aim of Pharmacogenetics, a branch of

Pharmacology [1,2]. Only on the basis of fully understanding one’s genetic polymorphism,

combined with pharmacogenetic studies, it will be possible to achieve a truly individualized treatment. This is the main goal of the so-called precision medicine that holds the promise that treatments will be tailored to every individual genome.

Certainly, the oncology field represents an area of great interest for Pharmacogenetics and the main field where precision medicine has actually made the best progress. According to the definition of the American National Cancer Institute, in oncology precision medicine “uses specific information about a person’s tumor to help make a diagnosis, plan

treatment, find out how well treatment is working, or make a prognosis” [3].

With the advent of next-generation sequencing (NGS) studies, tumor dynamic evolution and the genomic diversity within not only different tumors (intertumour heterogeneity) but also across single tumors have well been recognized (intratumor heterogeneity, ITH) [4]. ITH can be highly variable as a result of cellular (i.e. intercellular heterogeneity), genotypic (including genetic, epigenetic, transcriptomic and proteomic heterogeneity) and phenotypic (resulting from different extrinsic factors such as hypoxia, pH and extracellular signalling) changes. At a molecular level, tumor evolution and progression rise from the accumulation of independent genetic driver mutations in distinct tumor-cell subpopulations, both across different disease sites (spatial heterogeneity) and in the development time within a neoplastic lesion (temporal heterogeneity) [5]. These molecularly heterogeneous subclones have different capacities to grow, invade and metastasize and, harboring distinct molecular signatures, show differential levels of sensitivity to anticancer therapies [6]. Indeed, treatment exerts a selective pressure on cancer cells and only those that harbor mutations allowing to elude the killing effect of therapy, maintain an active independent signaling of growth, invasion and metastatization, may survive. Such alterations may pre-exist before treatment (primary resistance) or emerge de-novo in descendants of cells that have shown to be tolerant to the treatment (acquired resistance).

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This is what is generally observed in clinical practice: in the best-case scenario, an initial response is observed in a large portion of patients treated with drugs, however, resistance to therapy will eventually almost always arise [7, 8, 9, 10, 11].

Therefore, in an era where precision medicine is linked to Pharmacogenetics, the development of sensitive technologies able to catch tumor heterogeneity is needed, in order to identify the most appropriate treatment, increase the response rate, improve patients’ quality of life and reduce healthcare-related costs. All these data stress that there is an urgent need for new markers to improve diagnosis, prognostication, and prediction for patients with cancer, and these markers should preferentially be available sequentially under therapy.

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References

[1] Roses A. Nature. 2000 Jun 15;405(6788):857- Roses AD. Pharmacogenetics and the practice of

medicine. Nature. 2000 Jun 15;405(6788):857-65.

[2] Weinshilboum RM, Wang L. Pharmacogenomics: Precision Medicine and Drug

Response. Mayo Clin Proc. 2017 Nov;92(11):1711-1722.

[3] www.cancer.gov/publications/dictionaries/cancer-terms/def/precision-medicine. [4] McGranahan N, Swanton C. Clonal Heterogeneity and Tumor Evolution: Past, Present,

and the Future. Cell. 2017 Feb 9;168(4):613-628.

[5] Dagogo-Jack I, Shaw AT. Tumour heterogeneity and resistance to cancer therapies. Nat Rev Clin Oncol. 2018 Feb;15(2):81-94.

[6] Bhang HE, Ruddy DA, et al. Studying clonal dynamics in response to cancer therapy

using high-complexity barcoding. Nat Med. 2015 May;21(5):440-8.

[7] Sequist LV, Waltman BA, et al. Genotypic and histological evolution of lung cancers

acquiring resistance to EGFR inhibitors. Sci Transl Med. 2011 Mar 23;3(75):75ra26.

[8] Condorelli R, Spring L, et al. Polyclonal RB1 mutations and acquired resistance to CDK

4/6 inhibitors in patients with metastatic breast cancer. Ann Oncol. 2018 Mar

1;29(3):640-645.

[9] Zeng S, Pöttler M, et al. Chemoresistance in Pancreatic Cancer. Int J Mol Sci. 2019 Sep 11;20(18). pii: E4504.

[10] Ireland L, Santos A, et al. Chemoresistance in Pancreatic Cancer Is Driven by

Stroma-Derived Insulin-Like Growth Factors. Cancer Res. 2016 Dec 1;76(23):6851-6863.

[11] Antonarakis ES, Lu C, et al. AR-V7 and resistance to enzalutamide and abiraterone in

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How liquid biopsy can change clinical practice in oncology

Rationale

At present, in oncology decision-making tools are limited, with standard imaging modalities that could lead to overdiagnosis (particularly with mammography) or be quite challenging (like for pancreatic cancer) and serum biomarkers, like CA 19-9, that have low specificity and sensibility and could give false positive/ negative [1,2]. Moreover, the therapeutic strategies are defined according to the molecular landscape assessed with tissue biopsies and, thus, using DNA and/or RNA obtained from a fragment of the primary tumour or a single metastatic lesion. Anyway, mutational analysis in a single-site biopsy is encumbered by several limitations. First of all, the procedure can put the patient at risk and is, in some cases, rendered impossible due to the performance status of the patient. Secondly, tissue biopsy does not always provide sufficient material for molecular analysis. Thirdly, tissue biopsy may result in tumor heterogeneity underestimation and, due to ethical and practical considerations, it is not possible to take multiple biopsies simultaneously. Lastly, it is not suitable for longitudinal monitoring of clonal dynamic evolution [3].

A huge step forward in oncology to characterize tumor heterogeneity and plasticity at diagnosis and during the course of treatment was done with the use of a promising minimally invasive tool. All cells can release in human blood their materials, including cell-free DNA (cfDNA), RNA, proteins and vesicles (such as exosomes). Interestingly, cancer cells have a rapid turnover that result in a continuous release of tumor-derived nucleic acids and vesicles into the circulation, as well as cancer cells themselves can also separate from the tumour to enter the bloodstream (Figure 1). Therefore, the possibility to analyze the molecular landscape of solid tumours via a blood draw has attracted remarkable interest in the oncology field. This approach is a promising strategy for addressing the shortcomings of tissue sampling. The sampling and analysis of circulating tumour components present in the blood, defined as liquid biopsy, address the shortcomings of tissue sampling. It has a minimally invasive character and it allows a real-time follow-up of the genetic landscape of the tumor, both at diagnosis and during treatment. Indeed, it

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is the expression of the overall spatial heterogeneity from the overall sites of disease and it is able to identify emerging sub-clones responsible for treatment resistance [4,5]. This thesis will cover essentially circulating cell-free tumor DNA (cftDNA) and exosomes, that are explained in more depth below.

Figure 1. Schematic representation of circulating biomarkers. Using several mechanisms, tumor

cells release different circulating biomarker that freely swim into the bloodstream. Legend: cftDNA: circulating tumor DNA; CTCs: circulating tumor cells; EMT: epithelial-to-mesenchymal transition (Rofi E, et al. The emerging role of liquid biopsy in diagnosis, prognosis and treatment

monitoring of pancreatic cancer. Pharmacogenomics. 2019 Jan;20(1):49-68).

• Circulating cell-free tumor DNA

In 1948, circulating-free DNA was discovered in the blood of healthy individuals [6]; since then, several groups have shown the presence of DNA with neoplastic characteristics in the circulation [7]. Although the exact mechanism determining the release of cftDNA in blood is unclear, several hypotheses have been postulated including DNA release by tumor necrosis or apoptosis and lysis of circulating tumor cells or micrometastases [8]. Generally, cftDNA is detectable as small fragments with the length of 170-180 base pairs [8]. The concentration of cftDNA in blood varies considerably among different subjects ranging from 1 to 100 ng/ml, also depending on type and dimension of the tumor burden [9]. Moreover, due to the extensive contamination of DNA released from immune cells lysed during the clotting process in serum, plasma is considered a better source of cftDNA

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with the lower background levels of wild-type DNA [10]. High analytical sensitivity and specificity are therefore required for both cftDNA extraction and detection. In this regard, several technologies are available to analyze cftDNA mutations, including real-time PCR, automatic sequencing, mass spectrometry genotyping, NGS, digital PCR platforms (such as digital droplet PCR, ddPCR).

CftDNA potential clinical applications are numerous [11-14]: 1. cancer screening,

2. detect minimal residual disease after surgery with curative intent, 3. addresses treatment options,

4. monitor the response during treatment, anticipating in some cases the clinical progression and providing insights into the mechanisms of resistance and early treatment decision.

• Exosomes

Exosomes are 40–120 nm lipid bilayer membrane-bound vesicles derived from the endocytic pathway following the inward budding of multivesicular endosome fusion with the plasma membrane [15]. Following an exocytotic mechanism, exosomes are released in circulation from a wide range of cells, and several pieces of evidence suggest that tumor cells produce and secrete exosomes in increased amounts, compared with normal counterparts [16]. Their involvement in immune signalling, reprogramming of surrounding cells, as well as their ability to influence tumour microenvironment in favour of immune escape, therapy resistance, tumour growth and metastasis have been demonstrated [17]. Interestingly, they have been shown to contain proteins as well as a range of nucleic acids, including DNA, messanger RNAs, and micro RNAs of cells from which they are originated [17].

Tumor-derived exosomes may be investigated for their protein expression or genetic profile as diagnostic or prognostic markers. Moreover, RNAs and DNAs packaged in exosomes are protected from serum ribonucleases and deoxyribonuclease degradation. Therefore, their analysis offers the detection of mutations, splice variants, and gene fusions, as well as gene-expression profiling, providing additional diagnostic and

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prognostic information, as well as their usefulness for monitoring the treatment response [18-20].

Aims and outline of the thesis

The general aim of this thesis was the development of a pharmacogenetic-based approach to evaluate circulating predictive biomarkers during cancer treatments, in order to support routine clinical practice and improve the clinical outcome of cancer patients. The work is divided in four sections: every section is an overview of the potential use of cftDNA- or exosomes-based liquid biopsy during different treatments, i.e. targeted therapy (section I), hormonal therapy (section II), chemotherapy (section III) and immunotherapy (section IV).

In detail: • Section I

In chapter 1, there is an overview of the potential use of cftDNA-based liquid biopsy in personalized treatment in non-small cell lung cancer (NSCLC) patients carried mutations of the Epidermal Growth Factor Receptor gene (EGFR).

The identification of activating mutations of EGFR gene, including the in-frame deletions of exon 19 and the missense mutation L858R, has been used to select NSCLC patients with high probability of response to first- (gefitinib and erlotinib) or second-generation (afatinib) anti-EGFR tyrosine kinase inhibitors (TKIs) [21]. However, patients eventually develop progressive disease [22]. In approximately 50% of patients, resistance is due to the appearance of the gate-keeper mutation T790M of EGFR, that causing a steric hindrance affects the ability of the EGFR-TKIs to bind to the ATPkinase pocket. Moreover, the T790M mutation reduces the potency of competitive inhibitors in favor of the ATP [22]. Osimertinib is a third-generation EGFR-TKI and represents the new standard of care for T790M-positive patients [23]. It significantly improves clinical outcome but, again, resistance may develop and includes additional EGFR mutations (mainly C797S), KRAS, PIK3CA and BRAF V600E mutations, MET and HER-2 amplifications and small-cell transformation [24,25].

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On the basis of these considerations, data presented in this chapter highlight the usefulness of cftDNA as a good predictive biomarker in EGFR-mutated NSCLC treated with TKIs. These proof-of-concept studies provide strong evidence of the importance to identify the presence of the T790M/C797S mutations or to monitor their amount in order to predict tumor resistance treatment. Moreover, monitoring the increase of activating mutations of EGFR in serial plasma strongly suggests the development of resistance in osimertinib-treated patients. Finally, results also reinforce the prognostic significance of plasmatic mutational analysis in EGFR-mutated NSLC patients.

In chapter 2, there is an overview of the potential use of cftDNA-based liquid biopsy in personalized treatment in anaplastic lymphoma kinase (ALK) translocated NSCLC patients. ALK-positive NSCLC patients are highly sensitive to therapy with ALK tyrosine kinase inhibitors (TKIs), including crizotinib [26]. Nevertheless, disease progression (PD) inevitably occurs because of acquired resistance, for which ALK mutations are partly responsible and responsive to other TKIs, such as ceritinib, alectinib, and brigatinib [27,28]. Each ALK TKI shows different potency with respect to the point mutation. On these premises, data presented in this chapter highlight the usefulness of cftDNA as a good predictive biomarker in ALK-positive NSCLC treated with TKIs. In particular, results show that ALK and KRAS mutations can be isolated in plasma and their serial monitoring could serve as a response parameter.

In chapter 3, there is an overview of the potential use of exosomes-based liquid biopsy in personalized treatment in metastatic breast cancer patients.

Resistant to hormonal treatment is considered the main clinical challenge in the management of advanced breast cancer. The use of CDK4/6 inhibitors (CDK4/6i), such as palbociclib, ribociclib and abemaciclib, have demonstrated to be highly active in ER+/HER2- metastatic breast cancer patients progressed to hormonal therapy [29,30]. However, predictive biomarkers of response to these agents are lacking.

The proof-of concept of this study provides evidence that dynamic measurement of TK1 and CDK4/6/9 mRNA expression in plasma-derived exosomes is feasible and may provide useful information on clinical resistance to CDK4/6i.

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• Section II

In chapter 4, there is an overview of the potential use of exosomes-based liquid biopsy in personalized treatment in metastatic prostate cancer patients.

The androgen receptor (AR) is the major driver of prostate cancer growth and progression, and dysregulation of the pathway (i.e. amplification, point mutations and splice variants of the AR) may lead to a persistent AR signal transduction [18]. Abiraterone and enzalutamide represent the cornerstone of current treatment in this setting of patients. Anyway, a validated biomarker for predicting the outcome of hormonal therapy in castration-resistant prostate cancer (CRPC) is still lacking.

This chapter describes a fast, highly sensitive, and affordable method for the detection of biomarkers of resistance to hormonal therapy in RNA extracted from plasma-derived exosomes is described. Results confirm the role of the androgen receptor splice variant 7 (AR-V7) as biomarker of resistance to hormonal therapy in CRPC. Additionally, data show that resistance to hormonal therapy is better predicted by the availability of both AR-V7 status and the full-length androgen receptor (AR-FL).

• Section III

In chapter 5, there is an overview of the potential use of cftDNA-based liquid biopsy in personalized treatment in advanced pancreatic adenocarcinoma (PDAC).

Chemotherapy with FOLFIRINOX or gemcitabine plus nab paclitaxel represents the milestone of treatment of PDAC [31]. CA 19-9 is the only approved biomarker to monitor tumor response but it has several limitations (i.e. sensitivity and specificity) [32].

On the basis of these considerations and since KRAS is a driver oncogene occurring in over 90% of PDAC [33], data reported in this chapter highlight that the detection of KRAS mutations in plasma could represent a reliable non-invasive method to monitor chemotherapy response and improve predictability of disease outcome in PDAC.

• Section IV

In chapter 6, there is an overview of the potential use of exosomes-based liquid biopsy in personalized treatment in NSCLC and melanoma.

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Recently, immunotherapies have significantly improved the prognosis of several tumours, including NSCL and melanoma [34,35]. PD-L1 assessment in tumor tissue has been widely used to identify patients who will benefit from immunotherapies, but ITH may cause false negative results [36]. Moreover, it is well known that tumoral features, including PD-L1 expression, may vary throughout time in response to alterations in the tumor microenvironment and following the clonal selection induced by treatments [37].

Therefore, results presented in this chapter stress the feasibility at evaluating PD-L1 mRNA expression in plasma-derived exosomes to monitor response to immunotherapy in melanoma and NSCLC.

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[8] Jahr S, Hentze H, et al. DNA fragments in the blood plasma of cancer patients:

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[9] Heitzer E, Ulz P, Geigl JB. Circulating tumor DNA as a liquid biopsy for cancer. Clin Chem. 2015 Jan;61(1):112-23.

[10] Jung M, Klotzek S, et al. Changes in concentration of DNA in serum and plasma during

storage of blood samples. Clin Chem. 2003 Jun;49(6 Pt 1):1028-9.

[11] Beck J, Urnovitz HB, et al. Profile of the circulating DNA in apparently healthy

individuals. Clin Chem. 2009 Apr;55(4):730-8.

[12] Kim K, Shin DG, et al. Circulating cell-free DNA as a promising biomarker in patients

with gastric cancer: diagnostic validity and significant reduction of cfDNA after surgical resection. Ann Surg Treat Res. 2014 Mar;86(3):136-42.

[13] Del Re M, Petrini I, et al. Incidence of T790M in Patients With NSCLC Progressed to

Gefitinib, Erlotinib, and Afatinib: A Study on Circulating Cell-free DNA. Clin Lung Cancer.

2019 Oct 13:S1525-7304(19)30268-2.

[14] Cremolini C, Rossini D, et al. Rechallenge for Patients With RAS and BRAF Wild-Type

Metastatic Colorectal Cancer With Acquired Resistance to First-line Cetuximab and Irinotecan: A Phase 2 Single-Arm Clinical Trial. JAMA Oncol. 2019 Mar 1;5(3):343-350.

[15] Pan BT, Teng K, et al. Electron microscopic evidence for externalization of the

transferrin receptor in vesicular form in sheep reticulocytes. J Cell Biol. 1985

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[16] Parolini I, Federici C, et al. Microenvironmental pH is a key factor for exosome traffic

in tumor cells. J Biol Chem. 2009 Dec 4;284(49):34211-22.

[17] Kalluri R. The biology and function of exosomes in cancer. J Clin Invest. 2016 Apr 1;126(4):1208-15.

[18] Del Re M, Crucitta S, et al. Pharmacogenetics of androgen signaling in prostate

cancer: Focus on castration resistance and predictive biomarkers of response to treatment. Crit Rev Oncol Hematol. 2018 May;125:51-59.

[19] Del Re M, Marconcini R, et al. PD-L1 mRNA expression in plasma-derived exosomes is

associated with response to anti-PD-1 antibodies in melanoma and NSCLC. Br J Cancer.

2018 Mar 20;118(6):820-824.

[20] Del Re M, Bertolini I, et al. Overexpression of TK1 and CDK9 in plasma-derived

exosomes is associated with clinical resistance to CDK4/6 inhibitors in metastatic breast cancer patients. Breast Cancer Res Treat. 2019 Nov;178(1):57-62.

[21] Planchard D, Popat S, et al. Metastatic non-small cell lung cancer: ESMO Clinical

Practice Guidelines for diagnosis, treatment and follow-up. Ann Oncol. 2018 Oct

1;29(Suppl 4):iv192-iv237.

[22] Kobayashi S, Boggon TJ, et al. EGFR mutation and resistance of non-small-cell lung

cancer to gefitinib. N Engl J Med. 2005 Feb 24;352(8):786-92.

[23] Finlay MR, Anderton M, et al. Discovery of a potent and selective EGFR inhibitor

(AZD9291) of both sensitizing and T790M resistance mutations that spares the wild type form of the receptor. J Med Chem. 2014 Oct 23;57(20):8249-67.

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pharmacology and mechanisms of resistance. Pharmacogenomics. 2018 Jun

1;19(8):727-740.

[25] Del Re M, Tiseo M, et al. Contribution of KRAS mutations and c.2369C > T (p.T790M)

EGFR to acquired resistance to EGFR-TKIs in EGFR mutant NSCLC: a study on circulating tumor DNA. Oncotarget. 2017 Feb 21;8(8):13611-13619.

[26] Solomon BJ, Mok T, et al. First-line crizotinib versus chemotherapy in ALK-positive

lung cancer. N Engl J Med. 2014 Dec 4;371(23):2167-77.

[27] Gainor JF, Dardaei L, et al. Molecular Mechanisms of Resistance to First- and

Second-Generation ALK Inhibitors in ALK-Rearranged Lung Cancer. Cancer Discov. 2016

Oct;6(10):1118-1133.

[28] Shaw AT, Engelman JA. Ceritinib in ALK-rearranged non-small-cell lung cancer. N Engl J Med. 2014 Jun 26;370(26):2537-9.

[29] Finn RS, Crown JP, et al. The cyclin-dependent kinase 4/6 inhibitor palbociclib in

combination with letrozole versus letrozole alone as first-line treatment of oestrogen receptor-positive, HER2-negative, advanced breast cancer (PALOMA-1/TRIO-18): a randomised phase 2 study. Lancet Oncol. 2015 Jan;16(1):25-35.

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[30] Hortobagyi GN, Stemmer SM, et al. Updated results from MONALEESA-2, a phase III

trial of first-line ribociclib plus letrozole versus placebo plus letrozole in hormone receptor-positive, HER2-negative advanced breast cancer. Ann Oncol. 2018 Jul 1;29(7):1541-1547.

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pancreatic cancer. N Engl J Med. 2011 May 12;364(19):1817-25.

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diagnosis, prognosis and management of pancreatic adenocarcinoma: An evidence based appraisal. J Gastrointest Oncol. 2012 Jun;3(2):105-19.

[33] Eser S, Schnieke A, et al. Oncogenic KRAS signalling in pancreatic cancer. Br J Cancer. 2014 Aug 26;111(5):817-22.

[34] Reck M, Rodríguez-Abreu D, et al. Pembrolizumab versus Chemotherapy for

PD-L1-Positive Non-Small-Cell Lung Cancer. N Engl J Med. 2016 Nov 10;375(19):1823-1833.

[35] Robert C, Long GV, et al. Nivolumab in previously untreated melanoma without BRAF

mutation. N Engl J Med. 2015 Jan 22;372(4):320-30.

[36] Nakamura S, Hayashi K, et al. Intratumoral heterogeneity of programmed cell death

ligand-1 expression is common in lung cancer. PLoS One. 2017 Oct 19;12(10):e0186192.

[37] Jiang X, Wang J, et al. Role of the tumor microenvironment in PD-L1/PD-1-mediated

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SECTION I: Liquid biopsy and its

potential use during targeted therapy

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CHAPTER 1: Focus on EGFR-mutated NSCLC

Patients with NSCLC may display a low ratio of p.T790M vs.

activating EGFR mutations in plasma at disease progression:

implications for personalised treatment

Data here presented were published as original articles in:

Del Re M, Bordi P, Petrini I, Rofi E, Mazzoni F, Belluomini L, Vasile E, Restante G, Di Costanzo F, Falcone A, Frassoldati A, van Schaik RHN, Steendam CMJ, Chella A, Tiseo M, Morganti R, Danesi R. Patients with NSCLC may display a low ratio of p.T790M vs. activating EGFR mutations in plasma at disease progression: implications for personalised treatment. Oncotarget. 2017 Sep 15;8(49):86056-86065.

Introduction

Clonal heterogeneity of NSCLC has been well documented [1, 2] and contributes to the acquired resistance to targeted treatments [3-5]. There are multiple evidences that cells driving resistance in NSCLC are selected during treatment with EGFR-TKIs [6]. Several mechanisms have been described, including the gatekeeper EGFR mutation p.T790M, MET amplification, and HER-2 mutations [6-9]. The missense p.T790M is the substitution of a threonine (T) with a methionine (M) in codon 790; it is found in approximately 50-60% of NSCLCs after progression to EGFR-TKIs [10] and is the principal biomarker of resistance to EGFR-TKIs [7, 11, 12,13]. Approximately 0.32% to 78.95% of patients with NSCLC harboring EGFR activating mutations display p.T790M before administration of EGFR-TKI, although this percentage is variable according to test sensitivity [14]. As expected, subjects with a high p.T790M mutation burden had poorer clinical outcomes to EGFR-TKIs than patients with a low one [15]. It is therefore hypothesized that the selective pressure of EGFR-TKIs may select and favor the growth of p.T790M sub-clones, leading to acquired resistance. Although data on pretreatment p.T790M [16] and at progression to first-line EGFR-TKI [11, 17-20] are available, the information on the relationship between p.T790M levels and activating EGFR mutation in patients progressing to first-generation EGFR-TKI are lacking. The availability of a minimally invasive approach based on the analysis cftDNA may represent a suitable approach to interrogate ITH by capturing DNA

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released from multiple tumor sites [4, 8] and may dynamically monitor the molecular evolution of drug resistance. Detection of p.T790M in combination with clinical signs of disease progression would represent a critical information to guide the administration of p.T790M-active drugs, including osimertinib that represents the new standard of care on this group of NSCLC patients [21, 22]. The aim of the present study was to document the relationship between plasma levels of activating EGFR mutations and p.T790M at the time of progression to first-line EGFR TKIs in patients with NSCLC and provide initial information on the dynamics of cftDNA changes upon treatment with osimertinib.

Patients and Methods

The present study included 49 NSCLC patients carrying the p.T790M mutation in cftDNA. These patients were consecutively enrolled between June and October 2016 from a population of subjects with the following characteristics: 1) stage IIIb/IV disease carrying EGFR activating mutations (exon 19 deletions [ex19del] and/or exon 21 p.L858R) in tumor tissue at the time of initial diagnosis; 2) treatment with first- or second-generation EGFR-TKIs (gefitinib/erlotinib/afatinib), as per approved indication and 3) clinical and imaging evidence of disease progression, as per standard practice. In order to detect EGFR ex19del, p.L858R and p.T790M mutations, one blood sample was taken in each patient at the time of disease progression; in 5 subjects (enrolled in ASTRIS Trial [35]), 2 to 4 additional blood drawings were obtained to monitor EGFR ex19del, p.L858R and p.T790M mutations during osimertinib administration. Blood was sampled as per routine biochemistry testing, collected in EDTA tubes and centrifuged at 4°C for 10 min at 3000 rpm within two hours after drawing. Plasma samples (2 ml) were taken from material to be discarded and stored at −80°C until analysis. Circulating tumor DNA was extracted using the QIAmp Circulating Nucleic Acid Kit (Qiagen®, Valencia, CA, USA) following the manufacturer’s protocol. The ex19del, p.L858R and p.T790M alleles were examined using a digital droplet PCR (QX100™ Droplet Digital™ PCR System, BioRad®, Hercules, CA, USA) as previously described [8]. Droplets with a fluorescence intensity threshold higher than 3,000 were considered positive for the presence of mutations and results were given as copies of allele/ml. This study was compliant with local ethical practices; in particular,

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patients received treatments as per approved drug label and molecular tests were performed on residual material from standard diagnostic procedures.

Statistical analysis

Progression-free survival (PFS) was calculated from the first day of first-line EGFR-TKI treatment to radiological evidence of disease progression according to RECIST criteria; deaths for other causes than disease progression were considered events [36]. PFS of patients receiving osimertinib was not considered for statistical analysis. Before inferential statistics, an exploration phase was performed using box plots and scatter plots. We assessed three main quantitative factors: 1) concentration of the activating mutations (ex19del, p.L858R), 2) levels of p.T790M, and 3) their ratio (resistant/activating). In order to verify if the quantitative data were normally distributed we used the Kolmogorov–Smirnov and Shapiro–Wilks tests. A comparison between the three factors and PFS (all patients relapsed to first-line EGFR-TKI) was performed by a nonparametric correlation analysis (Spearman’s rank correlation coefficient), whereas Kruskal–Wallis and Mann–Whitney (two-tailed) tests were used to perform comparisons among the factor associated to the different treatment lines. Finally, the Wilcoxon test (two-tailed) has been performed to compare the number of copies/ml of plasmatic EGFR activating and p.T790M mutations. A p<0.05 was considered as statistically significant; all statistical analyses were performed using the SPSS version 24 software.

Results

A total of 49 patients were enrolled in this study; their characteristics are summarized in Table 1. The median PFS of the first-line EGFR-TKI treatment was 21 months (range 6-57 months). The activating mutations detected in cftDNA at the time of first-line EGFR-TKI progression were ex19del (42 subjects, 85.7%) and p.L858R (7 patients, 14.3%). The median values of ex19del, p.L858R and p.T790M were 3,550 (range 130-3,390,000), 420 (range 170-671,000) and 500 copies/mL (range 80-194,000) respectively. The median value of the ratio p.T790M/EGFR activating mutations was 0.26 (range, 0.0004-0.9) showing, overall, a low to very low ratio of p.T790M vs. activating mutations in patients

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at the time of progression to first/second-line EGFR-TKI (p<0,0001, Figure 1). The statistical analysis of PFS vs. the ratio of p.T790M/EGFR activating mutations showed no significant difference (p = 0.075; Spearman’s rank correlation coefficient = 0.256) suggesting that previous EGFR-TKI does not influence the ratio p.T790M/EGFR and that a high ratio is not required to obtain resistance to treatment (Figure 2). No differences in mutation amounts were seen between fast and slow progressing patients comparing months of PFS. The amount of ex19del, p.L858R and p.T790M EGFR mutant copies in plasma was monitored during treatment with osimertinib in 5 patients (2 with complete response - CR - to osimertinib, 2 with partial response – PR - and 1 with stable disease – SD - at first-tumor evaluation after 12 weeks) in order to gather information about the dynamics of EGFR mutational pattern as a function of therapy and time. The amount of EGFR mutant clones in plasma decreased in parallel (ex19del and p.T790M or p.L858R and p.T790M), although the activity of osimertinib is higher on p.T790M than ex19del and p.L858R and it would be, therefore, expected a steeper decline of p.T790M than ex19del and p.L858R. In general, the reduction of both ex19del and p.T790M was more marked than in the single patient bearing both p.T790M and p.L858R, which persisted in plasma (Figure 3). The decrease of mutated alleles in plasma was maintained during response to treatment.

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Figure 1. Plasma levels of EGFR activating mutations (ex19del and p. L858R) and p.T790M at the moment of disease progression to first/second-generation EGFR-TKI. The analysis showed

the very low ratio of p.T790M vs. activating mutations in patients at the time of progression to EGFR-TKI.

Figure 2. Absence of correlation between p. T790M/activating mutation ratio and PFS to

first/second-generation EGFR-TKI, highlighting that a high ratio is not required to obtain resistance to treatment, but also very low amounts drive the resistance.

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Figure 3. Decreasing of mutated EGFR alleles (ex19del, p. L858R, p.T790M) in plasma during treatment with osimertinib. The amount of EGFR mutant clones in plasma rapidly decreased in

parallel and was maintained during treatment, accordingly to the tumor response.

Discussion

The development of new drugs with improved targeting capability of EGFR activating mutations and with extended spectrum to p.T790M is an active area of research [23, 24]. Detection of mutant alleles can be performed in tissue as well as plasma, and ddPCR is a sensitive technology suitable for cftDNA detection and analysis [25]. Indeed, our previous work demonstrated that ddPCR detection of p.T790M in plasma reached a meaningful sensitivity of 81.8% and a specificity of 85.7%; in addition, the overall concordance between plasma and tissue analysis was good and corresponded to 83.3% [8]. Although the detection of p.T790M in EGFR-mutant NSCLC patients can be performed on both

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plasma and tumor tissue, the evolution of diagnostic approaches towards minimally-invasive procedures such as cftDNA is desirable for a number of reasons, including a) the invasive character of a tissue biopsy, b) unreachable tumor sites or insufficient tissue obtained after biopsy [26] and c) limitation of tissue biopsy in capturing tumor heterogeneity due to the small amount of tissue collected and number of tumor sites sampled. Indeed, ESMO clinical practice guidelines for diagnosis, treatment and follow-up of patients with NSCLC reports that if the p.T790M in peripheral blood is observed, treatment with third-generation EGFR TKIs is justified, while it recommends rebiopsy if cftDNA is negative for p.T790M [27]. Timely analysis of biomarkers to guide treatment decision is crucial and any delay in obtaining molecular testing results can postpone treatment decisions and reduce effectiveness of therapy for patients with advanced NSCLC [28]. p.T790M can be detected in plasma at a median time of 2.2 months prior to disease progression and is a predictive factor of resistance and disease outcome [29]. The role of p.T790M may change, depending on disease stage/phase. In particular, patients positive for p.T790M before treatment with EGFR-TKI show significantly inferior PFS (8.9 vs. 12.1 months) and overall survival (19.3 vs. 31.9 months) compared with those without p.T790M [30]. On contrary, at the time of disease progression after first- and second- generation EGFR-TKI, the presence of EGFR p.T790M is a favourable prognostic marker independently from the treatment with osimertinib [11]. p.T790M shows a complex biological behaviour, since p.T790M status in patients may change both temporally and spatially among tumor sites at least in part due to the selective pressure from EGFR-TKI [31]. Interestingly, p.T790M status of NSCLC varies after EGFR-TKI discontinuation and may change from positive to negative, thus justifying a potential re-challenge with EGFR-TKI [31]. Previous work provided initial evidence of the different amounts of p.T790M vs. activating mutations in plasma samples in 9 patients [32]. In addition, a recent report suggested a possible clinical importance for detection of p.T790M at low levels in plasma samples [18]. Longitudinal cftDNA analysis revealed an increase in plasma EGFR-activating mutation, and p.T790M announced rociletinib resistance in some patients, whereas in others the activating mutation increased but p.T790M remained suppressed [20]. The present work shows that patients progressing to first/second-generation EGFR-TKIs displayed a low amount of p.T790M compared to the EGFR activating mutations. Therefore, it can be speculated that either EGFR amplification could be a frequent finding

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in these patients, or that p.T790M is present in a minority of cells of the tumor cell population after TKI failure or both. Therefore, the intriguing questions are: 1) how frequent is EGFR amplification in these patients, 2) how p.T790M can drive tumor progression being so low within the tumor; 3) is there another mechanism of resistance in addition to p.T790M, and 4) is p.T790M able to synergize with activating EGFR mutation. Our data indicate that, even in presence of low amount of p.T790M, tumors respond to the third-generation TKI osimertinib, which is mainly active against p.T790M. The drug shows higher activity against the EGFR double mutants ex19del/p.T790M and p.L858R/p.T790M with respect to the EGFR bearing only the activating mutation as demonstrated by in vitro experiments. In particular, mean IC50values are 17 and 4 nM for

the ex19del and the p.L858R, respectively and 13 and 5 nM for the double mutants ex19del+p.T790M and p.L858R+p.T790M, respectively [6]. These data are confirmed also in patients of the present study because by monitoring the amount of residual EGFR mutations during osimertinib treatment, it is clear that cell clones carrying the double mutant ex19del/p.T790M or p.L858R/p.T790M and clones with the ex19del alone drastically decrease in their amount, while cell clones carrying the p.L858R alone remain detectable because of the low potency of osimertinib against this mutation. In patients monitored during osimertinib treatment, p.T790M reduction was marked and similar to the decline of plasma levels of EGFR activating mutations. The differences in concentration between them and the p.T790M may confirm that the tumor is heterogeneous, and it is composed by 1) wild-type clones, 2) cells carrying both the EGFR activating and p.T790M mutations, 3) clones with only the original EGFR activating mutation. A distinct population of cells with the p.T790M only is unlikely to be present. In conclusion, the present work demonstrates the feasibility of detecting very low amounts of p.T790M in plasma by a sensitive analytical approach and that this mutation is associated with tumor progression – and response to osimertinib – even though it may be a minority with respect to ex19del and p.L858R activating mutations. Further studies are warranted to gain additional knowledge on the interaction between EGFR mutations in TKI-resistant NSCLC, and to determine the clinical consequences to be connected to cftDNA outcomes in plasma. However, nowadays, is often required a treatment adaptation based on pharmacogenetic data [33, 34], and these results provide strong evidence supporting the usefulness of cftDNA as a good predictive biomarker.

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metastatic tumor in lung adenocarcinoma. BMC Cancer. 2016; 16:27.

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crizotinib resistance in ALK-rearranged lung cancer. J Thorac Oncol. 2013; 8:415-422.

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tumor cells by circulating tumor DNA of heterogeneous mutant EGFR genes in lung cancer.

Lung Cancer. 2016; 94:68-73.

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duration of epidermal growth factor receptor kinase inhibitor treatment in lung cancer.

Lung Cancer. 2016; 91:36-40.

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tyrosine kinase inhibitors against clinically relevant EGFR mutants in non-small-cell lung cancer. Oncotarget. 2015; 6:38789- 38803.

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105:2070-2075.

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to EGFR inhibitors and enhanced detection of the T790M mutation using a locked nucleic acid-based assay. Clin Cancer Res. 2011; 17:1169-1180.

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EGFR- mutant lung cancer: distinct natural history of patients with tumors harboring the T790M mutation. Clin Cancer Res. 2011; 17:1616-1622.

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with advanced nonsmall-cell lung cancer. Ann Oncol. 2015; 26:1415-1421.

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outcome in advanced NSCLC patients with acquired EGFR-TKI resistance. Sci Rep. 2016;

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[30] Wang Z, Chen R, et al. Quantification and dynamic monitoring of EGFR T790M in

plasma cell-free DNA by digital PCR for prognosis of EGFR-TKI treatment in advanced NSCLC. PLoS One. 2014; 9:e110780.

[31] Hata A, Katakami N, et al. Spatiotemporal T790M heterogeneity in individual patients

with EGFR-mutant non-small-cell lung cancer after acquired resistance to EGFR-TKI. J

Thorac Oncol. 2015; 10:1553-1559.

[32] Sorensen BS, Wu L, et al. Monitoring of epidermal growth factor receptor tyrosine

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advanced non-small cell lung cancer during treatment with erlotinib. Cancer. 2014;

120:3896-3901.

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perspectives. EPMA J. 2015; 6:9.

[34]Golubnitschaja O, Baban B, et al. Medicine in the early twenty-first century: paradigm

and anticipation - EPMA position paper 2016. EPMA J. 2016; 7:23.

[35] De Marinis F, Chul CB, et al. ASTRIS: A real world treatment study of osimertinib in

patients (pts) with EGFR T790M positive non-small cell lung cancer (NSCLC). J Clin Oncol.

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[36] Eisenhauer EA, Therasse P, et al. New response evaluation criteria in solid tumours:

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The amount of activating EGFR mutations in circulating cell free

DNA is a marker to monitor osimertinib response

Data here presented were published as original articles in:

Del Re M, Bordi P, Rofi E, Restante G, Valleggi S, Minari R, Crucitta S, Arrigoni E, Chella A, Morganti R, Tiseo M, Petrini I, Danesi R. The amount of activating EGFR mutations in circulating cell-free DNA is a marker to monitor osimertinib response. Br J Cancer. 2018 Nov;119(10):1252-1258.

Introduction

The knowledge regarding the kinetics of activating EGFR mutations (act-EGFR) during osimertinib treatment is still based on preliminary findings[1] and cfDNA could be a valuable tool to better understand tumour evolution, helping us identify predictive biomarkers of response. In the present study, we describe the changes of act-EGFR and T790M in cftDNA in relation to clinical outcome of patients treated with osimertinib and found that the amount of act-EGFR, but not of T790M, at baseline and during the treatment is a potential biomarker of response of patients treated with osimertinib.

Patients and Methods

A total of 34 NSCLC patients, taking part to the ASTRIS trial (NCT02474355), were enrolled in this study. Subjects must have (1) primary tumours positive for act-EGFR (exon 19 deletion [ex19del], exon 21 L858R, or other mutations [i.e., L861Q]), (2) PD after first- or second-generation EGFR-TKIs (gefitinib, erlotinib, or afatinib) associated with detectable T790M at liquid and/or tissue re-biopsy. Tissue analysis for act-EGFR (at diagnosis and, if available, at progression) and T790M (at progression, if available) was done by standard diagnostic procedures in use in each centre (i.e., EGFR TKI response®, Diatech, Jesi, Italy; Therascreen®, Qiagen, Valencia, CA). An additional requirement to be enrolled in this study was the presence of act-EGFR and T790M in cfDNA at baseline, as defined below. PFS is the time from assignment to treatment to PD or death from any cause. CR and PR, SD and PD are defined as per RECIST criteria v. 1.1 and were assessed at 3 months of treatment. Disease control rate (DCR) is defined as the percentage of patients who

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achieved CR, PR, and SD, while overall response rate (ORR) is the cumulative proportion of patients who have CR or PR.

Plasma samples for the analysis of cfDNA were taken before the first dose of osimertinib (baseline) and at the first clinical evaluation (after 3 months). Six milliliters of blood were collected in EDTA tubes and centrifuged for 10 min at 3000 r.p.m. at room temperature within 2 h after blood drawing. Full details of the method have been previously published [2]. Briefly, cfDNA was extracted using a QIAmp Circulating Nucleic Acid kit (Qiagen®) from 3 ml of plasma and the DNA was eluted in 100 μl of buffer. The analysis of cfDNA was performed by ddPCR using the ddPCR Mutation Assay (BioRad®, Hercules, CA). A fluorescence intensity threshold of 3000 was set as a cut-off point; the sample was considered as act-EGFR and T790M positive when at least one droplet was above the threshold level. act-EGFR and T790M values were reported as mutant allele frequency (MAF), defined as the proportion of mutant to wild-type PCR products in the ddPCR readout; T790M/act-EGFR MAF ratio was also calculated. Patients with PD at first assessment (3 months) underwent tissue biopsy, if feasible, and cfDNA C797S analysis, in addition to act-EGFR and T790M, to investigate the reason of resistance to osimertinib.

Statistical analysis

Before performing inferential analysis, an exploratory phase was carried out. To evaluate the normality of the quantitative data distributions, the Kolmogorov–Smirnov test was performed. The assessment of the paired data (matched and repeated) was performed by Wilcoxon’s test (two-tailed), whereas the evaluation of independent samples was performed with Kruskal–Wallis and Mann–Whitney (two-tailed) tests. Predictive value of T790M/act-EGFR MAF ratio at baseline was determined by receiver-operating curve (ROC) analysis and area under curve was assessed by a non-parametric test; the best cut-off was also calculated applying the Youden index. The dichotomous ratio calculated after ROC analysis was impacted with the PFS by Cox regression and the results were expressed by hazard ratios with confidence interval 95% and related p-value. Differences were considered significant at p < 0.05. All analyses, descriptive ad inferential, were carried out using the SPSS v.24 technology.

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Results

Table 1 summaries the demographic characteristics of patients. The act-EGFRs in tissues at diagnosis were ex19del in 25 subjects (73.5%), L858R in 8 patients (23.5%), and L861Q (3%) in 1 patient; interestingly, in this subject, ex19del was also found only in cfDNA at baseline and during the follow-up. All patients received a prior EGFR-TKI and 21 patients received > 1 line of therapy. The response rate to osimertinib in this population was 50%, with a DCR of 76.5% and a median PFS of 9.9 months; 8 patients showed PD at first evaluation. At baseline, the median act-EGFR MAF (2.6%) was significantly higher than T790M (0.575%, p < 0.0001; Figure 1). Act-EGFR MAF was related to disease control (p = 0.02; Figure 2), whereas T790M MAF was not (p = 0.8; Figure 2). Act-EGFR and T790M MAFs were related to previous lines of treatment; in particular, patients who received > 1 line of therapy had higher act-EGFR and T790M MAFs compared with patients who received one line of therapy (act-EGFR 6.2% vs. 1%, p = 0.01; T790M 0.6% vs. 0.2%, p = 0.05). No correlations were found between act-EGFR and T790M MAFs, and the number of tumour sites.

Table 1. Characteristics of patients.

Number of patients 34

Age (years, range) 63 (42–81)

Sex Male 10 (28.6%) Female 24 (71.4%) Smoking history Former 11 (32.3%) Never 23 (67.7%)

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35 Stage IV 32 (94.2%) IIIb 2 (5.8%) Prior EGFR-TKIs Gefitinib 21 (61.8%) Erlotinib 8 (23.5%) Afatinib 5 (14.7%)

Lines of treatment before osimertinib

First-line 13 (38.2%)

>1 line 21 (61.8%)

The median T790M/act-EGFR MAF ratio at baseline was 0.28 in the overall population and 0.35 vs. 0.18 in patients who achieved a disease control vs. PD, respectively (p = 0.006) (Figure 3). In the overall population, act-EGFR (2.6% to 0.2%, p = 0.002) and T790M (0.575% to 0%, p < 0.0001) were significantly decreased from baseline to 3 months (Figure 1). A disappearance or strong reduction of both act-EGFR and T790M MAFs were observed after 3 months in responding patients (CR + RP) and also in patients with SD, although the decline of act-EGFR was less pronounced compared with T790M (p = 0.002 and p < 0.0001, respectively). Eight patients had early PD during osimertinib treatment; in four of them there was a strong increase in act-EGFR MAF (patients 27, 28, 29 and 33), whereas in the other four it was decreased or undetectable. T790M disappeared from plasma in all patients but one (patient 33) despite evidence of PD. The reason of early resistance was demonstrated on re-biopsy (SCLC transformation in two patients, c-MET amplification one patient, C797S in another patient). In the remaining four subjects, tumour biopsy in one and cfDNA in three did not provide evidence of specific mechanism of resistance. Finally, a ROC curve analysis was performed to identify the best cut-off

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values of act-EGFR MAF and T790M/act-EGFR ratio and found, respectively, 2.6% and 0.22. The PFS of patients with act-EGFR MAF of > 2.6% and < 2.6%, were 10 months vs. not reached, respectively (p = 0.03; Figure 4), whereas patients with T790M/act-EGFR ≤ 0.22 had poorer PFS than patients with a value of > 0.22 (6 months vs. not reached, respectively, p = 0.01; Figure 5).

Figure 1. Act-EGFR and T790M MAF at baseline vs. 3 months. Data are expressed as MAF (%)

(outliers excluded).

Figure 2. Act-EGFR MAF in patients achieving CR/PR/SD vs. PD. Data are expressed as MAF (%)

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Figure 3. T790M/act-EGFR ratio in patients achieving CR/PR/SD vs PD. Data are expressed as

T790M/act-EGFR ratio.

Figure 4. PFS and OS stratified on the basis of cut-off value (2.6%) of act-EGFR MAF calculated by ROC analysis.

Figure 5. PFS of patients stratified on the basis of cut-off value (0.22) of T790M/act-EGFR ratio calculated after ROC analysis.

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Discussion

Osimertinib is designed to target T790M and act-EGFR more selectively than wild type EGFR [3]. As previously reported by our group [1] and confirmed in this work, at osimertinib baseline, the amount of the act-EGFR was significantly higher than T790M, indicating that cell clones resistant to first-/second-generation EGFR-TKIs represent a minority among cells bearing act-EGFR, and that they arise from the selective pressure of therapy. The lack of a significant correlation between baseline T790M MAF and disease response is not surprising, as demonstrated by the FLAURA trial (NCT02296125), showing a consistent benefit in terms of PFS of osimertinib vs. standard treatments (gefitinib/erlotinib) in EGFR-TKI-naïve patients not selected for T790M positivity [4]. These data suggest that osimertinib efficacy is not simply predicted by the presence of T790M. Moreover, other studies did not show a correlation between T790M level and response to osimertinib [5]. On the contrary, the act-EGFR MAF and the ratio of T790M/act-EGFR at baseline seemed to be reliable markers to predict the benefit of treatment; indeed, the risk of progression to osimertinib is higher in patients with elevated act-EGFR MAF and, therefore, with lower T790M/act-EGFR ratio. Interestingly, the cut-off of 0.22 of the T790M/act-EGFR ratio is able to discriminate patients with longer PFS, with a sensitivity of 81%. The potential predictive role of T790M/EGFR activating ratio was also observed in other studies [6,7]. In general, patients studied in our cohort presented a significant decrease in both act-EGFR and T790M at first evaluation, as compared with baseline. Although in a previous study [8] a clearance of plasma EGFR mutations at 6 weeks was associated with longer median PFS and better ORR, we did not find the same association in our population, as the majority of our patients had a complete clearance of T790M at 3 months, including all but one of those with PD. On the contrary, act-EGFR decreased in patients responding to treatment, whereas it was increased in four out of eight subjects with PD. Thus, our data suggest that cfDNA analysis of T790M is not useful to monitor response to osimertinib, whereas act-EGFR assessment is significantly associated with disease outcome and thus more informative. These data are in agreement with another publication indicating that T790M may disappear also in patients progressing to osimertinib [9], confirming that T790M is not a good biomarker to monitor response and tumour relapse during treatment. A

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previous case report documented the amplification of EGFR in cfDNA as a mechanism of resistance to osimertinib in a patient with increasing amount of mut-EGFR in cfDNA [10]. Several mechanisms of resistance to osimertinib have been described, including the EGFR C797S that abolishes the binding of osimertinib to EGFR, as well as G796S/R in addition to a hinge pocket L792F/H mutation [11]. Furthermore, c-MET amplification, [6,12] ERBB2, wild-type EGFR somatic copy-number alterations, L798I [6], and SCLC transformation [13] have been documented in patients resistant to third-generation EGFR-TKIs. The present study has two relevant limitations to consider: the retrospective nature of the analysis and the small sample size. For these reasons, future prospective studies with adequately sample size will be necessary to strengthen the results of the present work. In conclusion, this proof-of-concept study provides further evidence of the importance of T790M detection in plasma of patients resistant to first/second-generation EGFR-TKIs, in order to switch to osimertinib. However, act-EGFR proved to be a more reliable marker of response/resistance to treatment; thus, monitoring its increase in cfDNA in serial plasma samples strongly suggests the development of resistance in osimertinib-treated patients.

Liquid biopsy to monitor genetic alterations in plasma as non-invasive pharmacogenetic-based approach to evaluate predictive biomarkers during treatment in cancer patients.

PhD Course “Clinical and Translational Science”

Director: Prof. Stefano Del Prato

Doctorate Thesis

Liquid biopsy to monitor genetic alterations in plasma as

non-invasive pharmacogenetic-based approach to evaluate

predictive biomarkers during treatment in cancer patients

Candidate Tutors

Dr Eleonora Rofi

Prof. Romano Danesi

Dr Marzia Del Re

Table of contents

A te mio piccolo Tommaso.

“Lo studio e la ricerca della verità e della bellezza rappresentano una sfera

di attività in cui è permesso di rimanere bambini per tutta la vita.”

Pharmacogenetics and Precision Medicine in Oncology

References

How liquid biopsy can change clinical practice in oncology

Rationale

Aims and outline of the thesis

References

SECTION I: Liquid biopsy and its

potential use during targeted therapy

CHAPTER 1: Focus on EGFR-mutated NSCLC

Patients with NSCLC may display a low ratio of p.T790M vs.

activating EGFR mutations in plasma at disease progression:

implications for personalised treatment

Introduction

Patients and Methods

Statistical analysis

Results

Discussion

References

The amount of activating EGFR mutations in circulating cell free

DNA is a marker to monitor osimertinib response

Introduction

Patients and Methods

Statistical analysis

Results

Discussion