Literature DB >> 26240217

Academically led clinical trials: challenges and opportunities.

S Turajlic1, J Larkin2, C Swanton3.   

Abstract

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Year:  2015        PMID: 26240217      PMCID: PMC4576911          DOI: 10.1093/annonc/mdv332

Source DB:  PubMed          Journal:  Ann Oncol        ISSN: 0923-7534            Impact factor:   32.976


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Highlights from ASCO 2015 demonstrate the impasse we face in solid tumour oncology: the compelling novel immune and targeted therapies are often associated with cost–benefit ratios significantly above the thresholds for reimbursement. This is at least in part a consequence of our incomplete understanding of the mechanisms of response and resistance to these agents. For example, ipilimumab is associated with durable clinical benefit in 15%–20% of unselected advanced melanoma patients (∼£75 000 per patient treated), and while the responses to single-agent targeted therapies such as vemurafenib are higher, they are often relatively short-lived (∼£42 000 per median PFS of 6–7 months). New trial design strategies such as basket and umbrella studies have improved upon patient selection, but have not yielded detailed biological understanding of the drug targets, nor polygenic mechanisms of resistance within or between patients. Academically led studies have the opportunity and the responsibility to prioritize biological insights as trial end points, maximising research gain, increasing patient benefit/safety and ultimately, improving cost-effectiveness. Collection of tumour material is fundamental to these aims but the timing, handling and sample analysis are of critical importance (Figure 1).
Figure 1.

A schematic for biological sample collection throughout the course of disease and treatment. TILs, tumour-infiltrating lymphocytes; cfDNA, cell-free tumour DNA; PBMCs, peripheral mononuclear blood cells; PK, pharmacokinetic; PD, pharmacodynamic; PDX, patient-derived xenograft.

Resistance to targeted therapies can be mediated by pre-existing rather than de novo alterations. High resolution tracking of cancer cells in vitro demonstrated that only 10% of resistant clones arise de novo [1], while mathematical models of tumour growth suggest that radiographically detectable lesions harbour at least 10 resistant sub-clones [2]. Thus, comprehensive upfront tumour profiling could anticipate the genetic composition of such clone(s), while taking into account spatial and temporal tumour heterogeneity. Extensive sampling of metastatic sites at autopsy revealed 10 distinct PTEN alterations emerging under the selective pressure of PI(3)Kα inhibition [3], and five independent reversion events in a germline BRCA2 mutant carrier who progressed on olaparib and carboplatin [4]. Distinct mechanisms of BRAF and EGFR inhibitor resistance were detected across multiple metastases within individual patients with melanoma [5] and colorectal cancer [6], respectively. A schematic for biological sample collection throughout the course of disease and treatment. TILs, tumour-infiltrating lymphocytes; cfDNA, cell-free tumour DNA; PBMCs, peripheral mononuclear blood cells; PK, pharmacokinetic; PD, pharmacodynamic; PDX, patient-derived xenograft. The benefit of combination strategies can be limited by excess toxicity (combined targeting of the PI3K and MAPK pathways [7]), cross-resistance (BRAF and MEK inhibitors in melanoma [8]) and the persistent role of intra-tumour heterogeneity (targeting of the T790M EGFR mutation in lung cancer [9]). Informed by pre-clinical models, such as discontinuous dosing in BRAF-mutant melanoma [10], academically led trials can address more finely tuned ways of managing treatment resistance. In colorectal cancer cell-free tumour DNA (cfDNA) shows pulsatile levels of mutant KRAS in response to intermittent EGFR inhibition [11], providing the molecular rationale for re-challenge with targeted therapy. Similar frameworks are required to prospectively evaluate alternative or sequential scheduling as well as the role of cfDNA in tracking tumour progression. PD-L1 expression, a putative predictive marker for PD1/PDL1 inhibition, is also spatially heterogeneous [12]. Genomic data are a promising alternative biomarker in this area [13]. Mutational data, integrated with HLA typing, and tumour and peripheral T-cell profiling can define individual neo-antigenic repertoires. Academically led studies of immunotherapeutic agents must evaluate the ability of this approach to predict responses, inform immunotherapy/targeted combinations, and ultimately, facilitate adoptive T-cell therapy. Non-genetic causes of treatment resistance have been largely overlooked but studies that incorporate longitudinal biological sample collection and novel imaging techniques are well placed to examine tumour drug exposure (including heterogeneity of drug distribution [14]) and individual variation in drug metabolising enzymes, receptors, and transporters. Patient-derived xenografts can provide a useful platform for investigating personalised therapy in co-clinical trials [15], but only if robustly characterised and used in the full knowledge of their limitations (e.g. immunosuppressed host, mouse stroma and disparities in tumour burden between mouse and patient). There clearly are challenges to implementation of such complex studies but they can be overcome through close interdisciplinary work of academic/clinical consortia as illustrated by the Lung TRACERx programme [16], the use of measures such as one-time consent [17], post-mortem studies and stakeholder engagement (patient and public). In summary, we argue for a change of emphasis in drug development from learning little from many patients towards biologically rich clinical studies focussed on gleaning the maximum amount of biological information that might inform drug response and resistance for every patient entered into academic trial protocols.

disclosure

The authors have declared no conflicts of interest.
  17 in total

1.  Clonal evolution and resistance to EGFR blockade in the blood of colorectal cancer patients.

Authors:  Giulia Siravegna; Benedetta Mussolin; Michela Buscarino; Giorgio Corti; Andrea Cassingena; Giovanni Crisafulli; Agostino Ponzetti; Chiara Cremolini; Alessio Amatu; Calogero Lauricella; Simona Lamba; Sebastijan Hobor; Antonio Avallone; Emanuele Valtorta; Giuseppe Rospo; Enzo Medico; Valentina Motta; Carlotta Antoniotti; Fabiana Tatangelo; Beatriz Bellosillo; Silvio Veronese; Alfredo Budillon; Clara Montagut; Patrizia Racca; Silvia Marsoni; Alfredo Falcone; Ryan B Corcoran; Federica Di Nicolantonio; Fotios Loupakis; Salvatore Siena; Andrea Sartore-Bianchi; Alberto Bardelli
Journal:  Nat Med       Date:  2015-06-01       Impact factor: 53.440

2.  Timing and heterogeneity of mutations associated with drug resistance in metastatic cancers.

Authors:  Ivana Bozic; Martin A Nowak
Journal:  Proc Natl Acad Sci U S A       Date:  2014-10-27       Impact factor: 11.205

3.  Differential Expression of PD-L1 between Primary and Metastatic Sites in Clear-Cell Renal Cell Carcinoma.

Authors:  Marcella Callea; Laurence Albiges; Mamta Gupta; Su-Chun Cheng; Elizabeth M Genega; André P Fay; Jiaxi Song; Ingrid Carvo; Rupal S Bhatt; Michael B Atkins; F Stephen Hodi; Toni K Choueiri; David F McDermott; Gordon J Freeman; Sabina Signoretti
Journal:  Cancer Immunol Res       Date:  2015-05-26       Impact factor: 11.151

4.  Tunable-combinatorial mechanisms of acquired resistance limit the efficacy of BRAF/MEK cotargeting but result in melanoma drug addiction.

Authors:  Gatien Moriceau; Willy Hugo; Aayoung Hong; Hubing Shi; Xiangju Kong; Clarissa C Yu; Richard C Koya; Ahmed A Samatar; Negar Khanlou; Jonathan Braun; Kathleen Ruchalski; Heike Seifert; James Larkin; Kimberly B Dahlman; Douglas B Johnson; Alain Algazi; Jeffrey A Sosman; Antoni Ribas; Roger S Lo
Journal:  Cancer Cell       Date:  2015-01-15       Impact factor: 31.743

5.  Blockade of EGFR and MEK intercepts heterogeneous mechanisms of acquired resistance to anti-EGFR therapies in colorectal cancer.

Authors:  Sandra Misale; Sabrina Arena; Simona Lamba; Giulia Siravegna; Alice Lallo; Sebastijan Hobor; Mariangela Russo; Michela Buscarino; Luca Lazzari; Andrea Sartore-Bianchi; Katia Bencardino; Alessio Amatu; Calogero Lauricella; Emanuele Valtorta; Salvatore Siena; Federica Di Nicolantonio; Alberto Bardelli
Journal:  Sci Transl Med       Date:  2014-02-19       Impact factor: 17.956

6.  Modelling vemurafenib resistance in melanoma reveals a strategy to forestall drug resistance.

Authors:  Meghna Das Thakur; Fernando Salangsang; Allison S Landman; William R Sellers; Nancy K Pryer; Mitchell P Levesque; Reinhard Dummer; Martin McMahon; Darrin D Stuart
Journal:  Nature       Date:  2013-01-09       Impact factor: 49.962

Review 7.  Patient-derived xenograft models: an emerging platform for translational cancer research.

Authors:  Manuel Hidalgo; Frederic Amant; Andrew V Biankin; Eva Budinská; Annette T Byrne; Carlos Caldas; Robert B Clarke; Steven de Jong; Jos Jonkers; Gunhild Mari Mælandsmo; Sergio Roman-Roman; Joan Seoane; Livio Trusolino; Alberto Villanueva
Journal:  Cancer Discov       Date:  2014-07-15       Impact factor: 39.397

8.  Intra- and inter-tumor heterogeneity in a vemurafenib-resistant melanoma patient and derived xenografts.

Authors:  Kristel Kemper; Oscar Krijgsman; Paulien Cornelissen-Steijger; Aida Shahrabi; Fleur Weeber; Ji-Ying Song; Thomas Kuilman; Daniel J Vis; Lodewyk F Wessels; Emile E Voest; Ton Nm Schumacher; Christian U Blank; David J Adams; John B Haanen; Daniel S Peeper
Journal:  EMBO Mol Med       Date:  2015-09       Impact factor: 12.137

9.  Convergent loss of PTEN leads to clinical resistance to a PI(3)Kα inhibitor.

Authors:  Dejan Juric; Pau Castel; Malachi Griffith; Obi L Griffith; Helen H Won; Haley Ellis; Saya H Ebbesen; Benjamin J Ainscough; Avinash Ramu; Gopa Iyer; Ronak H Shah; Tiffany Huynh; Mari Mino-Kenudson; Dennis Sgroi; Steven Isakoff; Ashraf Thabet; Leila Elamine; David B Solit; Scott W Lowe; Cornelia Quadt; Malte Peters; Adnan Derti; Robert Schegel; Alan Huang; Elaine R Mardis; Michael F Berger; José Baselga; Maurizio Scaltriti
Journal:  Nature       Date:  2014-11-17       Impact factor: 49.962

10.  Tracking genomic cancer evolution for precision medicine: the lung TRACERx study.

Authors:  Mariam Jamal-Hanjani; Alan Hackshaw; Yenting Ngai; Jacqueline Shaw; Caroline Dive; Sergio Quezada; Gary Middleton; Elza de Bruin; John Le Quesne; Seema Shafi; Mary Falzon; Stuart Horswell; Fiona Blackhall; Iftekhar Khan; Sam Janes; Marianne Nicolson; David Lawrence; Martin Forster; Dean Fennell; Siow-Ming Lee; Jason Lester; Keith Kerr; Salli Muller; Natasha Iles; Sean Smith; Nirupa Murugaesu; Richard Mitter; Max Salm; Aengus Stuart; Nik Matthews; Haydn Adams; Tanya Ahmad; Richard Attanoos; Jonathan Bennett; Nicolai Juul Birkbak; Richard Booton; Ged Brady; Keith Buchan; Arrigo Capitano; Mahendran Chetty; Mark Cobbold; Philip Crosbie; Helen Davies; Alan Denison; Madhav Djearman; Jacki Goldman; Tom Haswell; Leena Joseph; Malgorzata Kornaszewska; Matthew Krebs; Gerald Langman; Mairead MacKenzie; Joy Millar; Bruno Morgan; Babu Naidu; Daisuke Nonaka; Karl Peggs; Catrin Pritchard; Hardy Remmen; Andrew Rowan; Rajesh Shah; Elaine Smith; Yvonne Summers; Magali Taylor; Selvaraju Veeriah; David Waller; Ben Wilcox; Maggie Wilcox; Ian Woolhouse; Nicholas McGranahan; Charles Swanton
Journal:  PLoS Biol       Date:  2014-07-08       Impact factor: 8.029

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