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      1 , 2 , 3 , 2 , 4 , 5 , 6 , 7 , 8 , 9 , 3 , 3 , 3 , 6 , 5 , 10 , 6 , 3 , 3 , 11 , 11 , 12 , 4 , 3 , 3 , 13 , 14 , 15 , 5 , 6 , 16 , 17 , 3 , 6 , 18 , 3 , 11 , 19 , 3 , NCI-MATCH Team

      JNCI: Journal of the National Cancer Institute

      Oxford University Press (OUP)

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          The proportion of tumors of various histologies that may respond to drugs targeted to molecular alterations is unknown. NCI-MATCH, a collaboration between ECOG-ACRIN Cancer Research Group (ECOG-ACRIN) and the National Cancer Institute (NCI), was initiated to find efficacy signals by matching patients with refractory malignancies to treatment targeted to potential tumor molecular drivers regardless of cancer histology.


          Trial development required assumptions about molecular target prevalence, accrual rates, treatment eligibility, enrollment rates, as well as consideration of logistical requirements. Central tumor profiling was performed with an investigational Next Generation DNA targeted Sequencing assay (NGS) of alterations in 143 genes, and protein expression of PTEN, MLH1, MSH2 and Rb. Treatments were allocated with a validated computational platform (MATCHBOX). A pre-planned interim analysis evaluated assumptions and feasibility in this novel trial.


          At interim analysis, accrual was robust, tumor biopsies were safe (< 1% severe events), and profiling success was 87.3%. Actionable molecular alteration frequency met expectations, but assignment and enrollment lagged due to histology exclusions and mismatch of resources to demand. To address this lag, we revised estimates of mutation frequencies, increased screening sample size, added treatments and improved assay throughput and efficiency (93.9% completion and 14-day turnaround).


          The experiences in the design and implementation of the NCI-MATCH trial suggest that profiling from fresh tumor biopsies and assigning treatment can be performed efficiently in a large national network trial. The success of such trials necessitates a broad screening approach and many treatment options easily accessible to patients.

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          Most cited references 23

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          EGF receptor gene mutations are common in lung cancers from "never smokers" and are associated with sensitivity of tumors to gefitinib and erlotinib.

           W Pao,  V. Miller,  M Zakowski (2004)
          Somatic mutations in the tyrosine kinase (TK) domain of the epidermal growth factor receptor (EGFR) gene are reportedly associated with sensitivity of lung cancers to gefitinib (Iressa), kinase inhibitor. In-frame deletions occur in exon 19, whereas point mutations occur frequently in codon 858 (exon 21). We found from sequencing the EGFR TK domain that 7 of 10 gefitinib-sensitive tumors had similar types of alterations; no mutations were found in eight gefitinib-refractory tumors (P = 0.004). Five of seven tumors sensitive to erlotinib (Tarceva), a related kinase inhibitor for which the clinically relevant target is undocumented, had analogous somatic mutations, as opposed to none of 10 erlotinib-refractory tumors (P = 0.003). Because most mutation-positive tumors were adenocarcinomas from patients who smoked <100 cigarettes in a lifetime ("never smokers"), we screened EGFR exons 2-28 in 15 adenocarcinomas resected from untreated never smokers. Seven tumors had TK domain mutations, in contrast to 4 of 81 non-small cell lung cancers resected from untreated former or current smokers (P = 0.0001). Immunoblotting of lysates from cells transiently transfected with various EGFR constructs demonstrated that, compared to wild-type protein, an exon 19 deletion mutant induced diminished levels of phosphotyrosine, whereas the phosphorylation at tyrosine 1092 of an exon 21 point mutant was inhibited at 10-fold lower concentrations of drug. Collectively, these data show that adenocarcinomas from never smokers comprise a distinct subset of lung cancers, frequently containing mutations within the TK domain of EGFR that are associated with gefitinib and erlotinib sensitivity.
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            New response evaluation criteria in solid tumours: revised RECIST guideline (version 1.1).

            Assessment of the change in tumour burden is an important feature of the clinical evaluation of cancer therapeutics: both tumour shrinkage (objective response) and disease progression are useful endpoints in clinical trials. Since RECIST was published in 2000, many investigators, cooperative groups, industry and government authorities have adopted these criteria in the assessment of treatment outcomes. However, a number of questions and issues have arisen which have led to the development of a revised RECIST guideline (version 1.1). Evidence for changes, summarised in separate papers in this special issue, has come from assessment of a large data warehouse (>6500 patients), simulation studies and literature reviews. HIGHLIGHTS OF REVISED RECIST 1.1: Major changes include: Number of lesions to be assessed: based on evidence from numerous trial databases merged into a data warehouse for analysis purposes, the number of lesions required to assess tumour burden for response determination has been reduced from a maximum of 10 to a maximum of five total (and from five to two per organ, maximum). Assessment of pathological lymph nodes is now incorporated: nodes with a short axis of 15 mm are considered measurable and assessable as target lesions. The short axis measurement should be included in the sum of lesions in calculation of tumour response. Nodes that shrink to <10mm short axis are considered normal. Confirmation of response is required for trials with response primary endpoint but is no longer required in randomised studies since the control arm serves as appropriate means of interpretation of data. Disease progression is clarified in several aspects: in addition to the previous definition of progression in target disease of 20% increase in sum, a 5mm absolute increase is now required as well to guard against over calling PD when the total sum is very small. Furthermore, there is guidance offered on what constitutes 'unequivocal progression' of non-measurable/non-target disease, a source of confusion in the original RECIST guideline. Finally, a section on detection of new lesions, including the interpretation of FDG-PET scan assessment is included. Imaging guidance: the revised RECIST includes a new imaging appendix with updated recommendations on the optimal anatomical assessment of lesions. A key question considered by the RECIST Working Group in developing RECIST 1.1 was whether it was appropriate to move from anatomic unidimensional assessment of tumour burden to either volumetric anatomical assessment or to functional assessment with PET or MRI. It was concluded that, at present, there is not sufficient standardisation or evidence to abandon anatomical assessment of tumour burden. The only exception to this is in the use of FDG-PET imaging as an adjunct to determination of progression. As is detailed in the final paper in this special issue, the use of these promising newer approaches requires appropriate clinical validation studies.
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              Molecularly targeted therapy based on tumour molecular profiling versus conventional therapy for advanced cancer (SHIVA): a multicentre, open-label, proof-of-concept, randomised, controlled phase 2 trial.

              Molecularly targeted agents have been reported to have anti-tumour activity for patients whose tumours harbour the matching molecular alteration. These results have led to increased off-label use of molecularly targeted agents on the basis of identified molecular alterations. We assessed the efficacy of several molecularly targeted agents marketed in France, which were chosen on the basis of tumour molecular profiling but used outside their indications, in patients with advanced cancer for whom standard-of-care therapy had failed.

                Author and article information

                JNCI: Journal of the National Cancer Institute
                Oxford University Press (OUP)
                January 10 2020
                January 10 2020
                [1 ]Massachusetts General Hospital, Boston, MA
                [2 ]Dana Farber Cancer Institute ECOG-ACRIN Biostatistics Center, Boston, MA
                [3 ]Division of Cancer Treatment and Diagnosis, National Cancer Institute, NIH, Bethesda, MD
                [4 ]Center for Biomedical Informatics and Information Technology, National Cancer Institute, NIH, Bethesda, MD
                [5 ]University of Texas MD Anderson Cancer Center, Houston, TX
                [6 ]Frederick National Laboratory for Cancer Research, Frederick, MD
                [7 ]Thomas Jefferson University Hospital, Philadelphia, PA
                [8 ]Massachusetts General Hospital and Harvard University, Boston, MA
                [9 ]Yale University, New Haven, CT
                [10 ]Center for Biomedical Informatics and Information Technology, Frederick National Laboratory for Cancer Research, Frederick, MD
                [11 ]ECOG-ACRIN Cancer Research Group, Philadelphia PA
                [12 ]ECOG-ACRIN Cancer Research Group, Boston, MA
                [13 ]Radiation Oncology, Dana Farber Cancer Institute, Boston MA
                [14 ]Mayo Clinic, Rochester, MN
                [15 ]Herbert Irving Comprehensive Cancer Center, Columbia University, New York, NY
                [16 ]Division of Cancer Prevention, National Cancer Institute, NIH, Bethesda, MD
                [17 ]Case Comprehensive Cancer Center, Case Western Reserve University, Cleveland OH
                [18 ]University of Texas Southwestern Simmons Cancer Center, Dallas, TX
                [19 ]University of Pennsylvania, Philadelphia, PA
                © 2020


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