Randomized phase II trial comparing molecularly targeted therapy based on tumor molecular profiling versus conventional therapy in patients with refractory cancer: cross-over analysis from the SHIVA trial

Belin, Lisa; Kamal, Maud; Mauborgne, Cécile; Plancher, Corine; Mulot, F; Delord, J.; Gonçalvès, Anthony; Gavoille, Céline; Dubot, Coraline; Isambert, Nicolás; Campone, Mario; Trédan, Olivier; Ricci, Francesco; Alt, Marie; Loirat, Delphine; Mp, Sablin; Paolettí, Xavier; Servois, Vincent; Tourneau, Christophe Le

doi:10.1093/annonc/mdw666

Cited by 64 publications

(54 citation statements)

References 10 publications

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“…Patients in SHIVA01 were allowed to cross over at disease progression in either arm. The propor tion of patients who had a PFS2toPFS1 ratio exceed ing 1.3 in the subgroup of patients who crossed over in SHIVA01 compared favorably with the results obtained from the von Hoff study and in MOSCATO (37 vs 27 and 33%) [13,16,18].…”

supporting

confidence: 67%

The SHIVA01 Trial: What Have we Learned?

2017

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supporting

confidence: 67%

The SHIVA01 Trial: What Have we Learned?

2017

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“…The ratio of PFS of algorithm-based treatment selection over the-physician-choice exceeded 1.3 in 37% of patients who switched to the algorithm-guided treatment arm. Contrastingly, 61% of patients who crossed over from the algorithm arm to the-physician-choice arm exceeded prior PFS by a factor of 1.3 36 . (We are smarter than the computers, aren’t we?)…”

Section: Rethinking the Clinical Trials Conductmentioning

confidence: 95%

“…These experiments drastically change the definition of success and target outcomes. A recent SHIVA trial (Molecularly targeted therapy based on tumour molecular profiling versus conventional therapy for advanced cancer) 36 used the ratio of progression-free survival (PFS) on genotype-matched treatment to PFS on genotype-unmatched treatment to assess the efficacy of therapy guided by patients’ tumor molecular profiling. This particular estimation of the clinical efficacy was used to compensate for the extreme cohort heterogeneity of tumor types.…”

Section: Rethinking the Clinical Trials Conductmentioning

confidence: 99%

“…SHIVA 36 , Signature trial by Novartis 38 ) suggest that the lower response rates in the range of 2–4% are underestimating the clinical benefit accounted for as prolonged disease stability which translates to a greater PFS. The Signature trial is particularly worth mentioning as it utilized uniquely a patient-specific expedited IRB approval process and the site initiation process breaking the restrictive administrative thresholds.…”

Section: Rethinking the Clinical Trials Conductmentioning

confidence: 99%

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Future Clinical Trials

Astsaturov

2017

Surgical Oncology Clinics of North America

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SYNOPSIS The design of modern oncology clinical trials seeks to match patients’ cancer molecular biomarkers with medications that specifically target those biomarkers- a general paradigm shift in cancer care coined as “clinical cancer biology”1. This approach exploits the synthetic lethality between a specific genetic alteration in the cancer cell and a drug: rapid termination of exaggerated kinase activity (e.g. BCR-ABL, HER2 amplification), or irrevocable DNA damage (e.g. PARP inhibitors, platinum in BRCA-mutated cancers) exemplify this phenomenon. Synthetic lethality-based investigations are driven by rapidly evolving technologies for cancer molecular profiling including not only direct testing of the tumor DNA but also blood-based biomarkers such as oncogenic metabolites and cancer specific DNA fragments in biological fluids. As these technologies evolve, future clinical trials will test drug’s activity based on the molecular mechanisms, rather than by the tumor’s appearance under a microscope.

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“…These results are comparable to other retrospective analyses, including one meta-analysis that showed the potential benefit of a precision medicine approach [7,8] . However, it should be noted that the only randomised trial to date failed to show the benefit of a targeted approach as compared to treatment according to the doctor's choice [9] , even when a third of the patients crossing over to targeted treatment exhibited an improved PFS ratio [10] .…”

Section: Introductionmentioning

confidence: 99%

Cancer Precision Medicine: Why More Is More and DNA Is Not Enough

Schütte

Ogilvie

Rieke

et al. 2017

Public Health Genomics

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Every tumour is different. They arise in patients with different genomes, from cells with different epigenetic modifications, and by random processes affecting the genome and/or epigenome of a somatic cell, allowing it to escape the usual controls on its growth. Tumours and patients therefore often respond very differently to the drugs they receive. Cancer precision medicine aims to characterise the tumour (and often also the patient) to be able to predict, with high accuracy, its response to different treatments, with options ranging from the selective characterisation of a few genomic variants considered particularly important to predict the response of the tumour to specific drugs, to deep genome analysis of both tumour and patient, combined with deep transcriptome analysis of the tumour. Here, we compare the expected results of carrying out such analyses at different levels, from different size panels to a comprehensive analysis incorporating both patient and tumour at the DNA and RNA levels. In doing so, we illustrate the additional power gained by this unusually deep analysis strategy, a potential basis for a future precision medicine first strategy in cancer drug therapy. However, this is only a step along the way of increasingly detailed molecular characterisation, which in our view will, in the future, introduce additional molecular characterisation techniques, including systematic analysis of proteins and protein modification states and different types of metabolites in the tumour, systematic analysis of circulating tumour cells and nucleic acids, the use of spatially resolved analysis techniques to address the problem of tumour heterogeneity as well as the deep analyses of the immune system of the patient to, e.g., predict the response of the patient to different types of immunotherapy. Such analyses will generate data sets of even greater complexity, requiring mechanistic modelling approaches to capture enough of the complex situation in the real patient to be able to accurately predict his/her responses to all available therapies.

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Randomized phase II trial comparing molecularly targeted therapy based on tumor molecular profiling versus conventional therapy in patients with refractory cancer: cross-over analysis from the SHIVA trial

Abstract: The cross-over analysis of the SHIVA trial identified 37% of patients who crossed-over from TPC to MTA with a PFSMTA/PFSTPC ratio exceeding 1.3.

Cited by 64 publications

References 10 publications

The SHIVA01 Trial: What Have we Learned?

The SHIVA01 Trial: What Have we Learned?

Future Clinical Trials

Cancer Precision Medicine: Why More Is More and DNA Is Not Enough

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