Concordance of ATM (ataxia telangiectasia mutated) immunohistochemistry between biopsy or metastatic tumor samples and primary tumors in gastric cancer patients.

ATM (ataxia telangiectasia mutated) is one of several DNA repair proteins that are suggested to sensitize tumor cells to the poly(ADP-ribose) polymerase inhibitor olaparib when deficient. The aim of this study was to assess the spatiotemporal concordance of ATM immunohistochemistry (IHC) in gastric cancer in order to determine if measurements made at the level of various sample types and times could be inferred as having the potential to be relevant to treatment decisions made at the patient level. Two independent cohorts composed of 591 gastric cancer patients divided into a gastrectomy cohort (n = 450) and a metastasis cohort (n = 141) were used in this study. A total of 2,705 ATM IHC samples were examined, including 450 whole tissue, 3 sets of 450 tissue microarray (TMA), 301 biopsy, 222 metastatic tumor and 2 additional whole tissue samples of 50 cases from the gastrectomy cohort, and 141 pairs of primary and metastatic tumors from the metastasis cohort. The prevalence of ATM negativity was 13.1% in biopsies, 13.9, 15.1, and 16.0% in TMAs and 15.9% in whole tissue samples of the gastrectomy cohort, and 21.4% in primary tumor and 21.5% in metastatic tumor samples of the metastasis cohort. coefficients were 0.341 for biopsy, 0.572 as the average of 3 TMAs and 0.415 for the largely synchronous metastatic tumors of the gastrectomy cohort, and 0.153 for the largely asynchronous metastatic tumors of the metastasis cohort. Using whole tissue sections from tumor resections or primary tumor, respectively, as the reference standards, specificity and sensitivity were 91.6 and 41.0% for biopsy, 93.9 and 61.9% as the average of 3 TMAs, and 86.6 and 58.8% for metastatic tumors of the gastrectomy cohort and 81.7 and 33.3% for metastatic tumors of the metastasis cohort, respectively. Although we have demonstrated that the IHC assay for ATM was robust and reproducible in gastric tumor samples, we have also found that measurements were subject to significant discordance across multiple sample types from the same patient. Further work will be necessary to determine if classification may be made more consistent by multiple sampling. However, the lack of agreement between primary and asynchronous metastatic samples suggests that such sampling would need to be performed at the time of any treatment decision.

Practical perspectives of personalized healthcare in oncology.

There is an increasing prevalence of drug-diagnostic combinations in oncology. This has placed diagnostic stakeholders directly into the complex benefit-risk, cost, value and uncertainty-driven development paradigm traditionally the preserve of the drug development community. In this review we focus on the delivery of the clinical data required to advance such drug-diagnostic combination development programmes and ultimately satisfy regulators and payors of the value of contemporaneous changes in diagnostic and treatment practice. Ideally all stakeholders would like to initially estimate, and ultimately specify, the comparative benefit-risk for a new treatment option with and without changing diagnostic practice. Hence, in an ideal world clinical trial design is focused on acquiring biomarker treatment interaction data. In this review we describe the key scientific and feasibility inputs required to design and deliver such trials and the drivers, advantages and disadvantages associated with departing from this model. We do not discuss the discovery of new biomarkers nor the analytical validation and marketing of diagnostic products. Following on from trial design we describe how subsequent success then depends upon the concepts that guide trial design being driven into the complex world of large, multinational clinical trial delivery. For every aspect of a traditional clinical drug trial such as supply, recruitment and adherence, there is a corresponding concept for the diagnostic element. In practice, this means that each patient's contribution to the decision making data-set is subject to double jeopardy (attrition on clinical outcome and biomarker status). Historically, this has led to significantly reduced power for detecting biomarker-treatment interactions, reduced decision making confidence and a waste of valuable human and financial resources. We describe recent practice changes and experience that have led to the successful delivery of such trials focusing on both pre- and on trial aspects. The former includes the pivotal role of tissue banks in accurate estimation of evaluability and prevalence for biomarker assays and the latter several practices designed to engage and incentivize key stakeholders particularly CRAs and pathologists. The result is that in the new world of developing personalized treatments for cancer patients the real-time acquisition and monitoring of biomarker data receives similar support to that traditionally reserved for clinical outcome data and far more patients contribute to the testing of personalized medicine hypotheses.