Jan Bogaerts

Purpose:Currently, guidelines for PET with 18F-fluorodeoxyglucose (FDG-PET) interpretation for assessment of therapy response in oncology primarily involve visual evaluation of FDG-PET/CT scans. However, quantitative measurements of the metabolic activity in tumors may be even more useful in evaluating response to treatment. Guidelines based on such measurements, including the European Organization for Research and Treatment of Cancer Criteria and PET Response Criteria in Solid Tumors, have been proposed. However, more rigorous analysis of response criteria based on FDG-PET measurements is needed to adopt regular use in practice.Experimental Design:Well-defined boundaries of repeatability and reproducibility of quantitative measurements to discriminate noise from true signal changes are a needed initial step. An extension of the meta-analysis from de Langen and colleagues (2012) of the test–retest repeatability of quantitative FDG-PET measurements, including mean, maximum, and peak standardized uptake values (SUVmax, SUVmean, and SUVpeak, respectively), was performed. Data from 11 studies in the literature were used to estimate the relationship between the variance in test–retest measurements with uptake level and various study-level, patient-level, and lesion-level characteristics.Results:Test–retest repeatability of percentage fluctuations for all three types of SUV measurement (max, mean, and peak) improved with higher FDG uptake levels. Repeatability in all three SUV measurements varied for different lesion locations. Worse repeatability in SUVmean was also associated with higher tumor volumes.Conclusions:On the basis of these results, recommendations regarding SUV measurements for assessing minimal detectable changes based on repeatability and reproducibility are proposed. These should be applied to differentiate between response categories for a future set of FDG-PET–based criteria that assess clinically significant changes in tumor response.

Early indicators of metastatic cancer response to therapy are important for evaluating new drugs and stopping ineffective treatment. The RECIST guidelines based on repeat cancer imaging are widely adopted in clinical trials, are used to identify active regimens that may change practice, and contribute to regulatory approvals. However, these criteria do not provide insight before 6 to 12 weeks of treatment and typically require that patients have measurable disease. Recent data suggest that measuring on-treatment changes in the amount or proportion of ctDNA in peripheral blood plasma may accurately identify responding and nonresponding cancers at earlier time points. Over the past year, the RECIST working group has evaluated current evidence for plasma ctDNA kinetics as a treatment response biomarker in metastatic cancers and early endpoint in clinical trials to identify areas of focus for future research and validation. Here, we outline the requirement for large standardized trial datasets, greater scrutiny of optimal ctDNA collection time points and assay thresholds, and consideration of regulatory body guidelines and patient opinions. In particular, clinically meaningful changes in plasma ctDNA abundance are likely to differ by cancer type and therapy class and must be assessed before ctDNA can be considered a potential pan-cancer response evaluation biomarker. Despite the need for additional data, minimally invasive on-treatment ctDNA measurements hold promise to build upon existing response assessments such as RECIST and offer opportunities for developing novel early endpoints for modern clinical trials.

Purpose:Currently, guidelines for PET with 18F-fluorodeoxyglucose (FDG-PET) interpretation for assessment of therapy response in oncology primarily involve visual evaluation of FDG-PET/CT scans. However, quantitative measurements of the metabolic activity in tumors may be even more useful in evaluating response to treatment. Guidelines based on such measurements, including the European Organization for Research and Treatment of Cancer Criteria and PET Response Criteria in Solid Tumors, have been proposed. However, more rigorous analysis of response criteria based on FDG-PET measurements is needed to adopt regular use in practice.Experimental Design:Well-defined boundaries of repeatability and reproducibility of quantitative measurements to discriminate noise from true signal changes are a needed initial step. An extension of the meta-analysis from de Langen and colleagues (2012) of the test–retest repeatability of quantitative FDG-PET measurements, including mean, maximum, and peak standardized uptake values (SUVmax, SUVmean, and SUVpeak, respectively), was performed. Data from 11 studies in the literature were used to estimate the relationship between the variance in test–retest measurements with uptake level and various study-level, patient-level, and lesion-level characteristics.Results:Test–retest repeatability of percentage fluctuations for all three types of SUV measurement (max, mean, and peak) improved with higher FDG uptake levels. Repeatability in all three SUV measurements varied for different lesion locations. Worse repeatability in SUVmean was also associated with higher tumor volumes.Conclusions:On the basis of these results, recommendations regarding SUV measurements for assessing minimal detectable changes based on repeatability and reproducibility are proposed. These should be applied to differentiate between response categories for a future set of FDG-PET–based criteria that assess clinically significant changes in tumor response.

Purpose: Currently, guidelines for PET with 18F-fluorodeoxyglucose (FDG-PET) interpretation for assessment of therapy response in oncology primarily involve visual evaluation of FDG-PET/CT scans. However, quantitative measurements of the metabolic activity in tumors may be even more useful in evaluating response to treatment. Guidelines based on such measurements, including the European Organization for Research and Treatment of Cancer Criteria and PET Response Criteria in Solid Tumors, have been proposed. However, more rigorous analysis of response criteria based on FDG-PET measurements is needed to adopt regular use in practice. Experimental Design: Well-defined boundaries of repeatability and reproducibility of quantitative measurements to discriminate noise from true signal changes are a needed initial step. An extension of the meta-analysis from de Langen and colleagues (2012) of the test–retest repeatability of quantitative FDG-PET measurements, including mean, maximum, and peak standardized uptake values (SUVmax, SUVmean, and SUVpeak, respectively), was performed. Data from 11 studies in the literature were used to estimate the relationship between the variance in test–retest measurements with uptake level and various study-level, patient-level, and lesion-level characteristics. Results: Test–retest repeatability of percentage fluctuations for all three types of SUV measurement (max, mean, and peak) improved with higher FDG uptake levels. Repeatability in all three SUV measurements varied for different lesion locations. Worse repeatability in SUVmean was also associated with higher tumor volumes. Conclusions: On the basis of these results, recommendations regarding SUV measurements for assessing minimal detectable changes based on repeatability and reproducibility are proposed. These should be applied to differentiate between response categories for a future set of FDG-PET–based criteria that assess clinically significant changes in tumor response.

Background: Biology has become the main driver of breast cancer therapy. Intrinsic biological subtypes by gene expression profiling have been identified. Pathology can be used to define surrogates of these subtypes but these are not always concordant, which may lead to different treatment plans. We investigated the concordance between BluePrint and MammaPrint (microarray-based) breast cancer subtypes and pathological surrogates (based on ER, PR, HER2 & Ki67). Methods: Using available data (centrally assessed pathology and genomics) from the MINDACT pilot phase [Rutgers et al. EJC 2011] 621 tumors were analyzed. Patients were classified according to 4-category based pathology (ER, PR, HER2 and Ki67); additionally, classification was performed adhering to the recent St. Gallen recommendations [Goldhirsch et al. 2011], which recognizes an additional category (Luminal B HER2+). Based on BluePrint and MammaPrint 4 subtypes are formed: Luminal A (Luminal-type/MammaPrint Low Risk); Luminal B (Luminal-type/MammaPrint High Risk); HER2-type; and Basal-type. This study is an analysis of discordant patient groups (i.e. clinical HER2+/BluePrint Luminal-type; clinical Hormone Receptor (HR)-positive/BluePrint Basal-type) providing comparison of centrally assessed tumor heterogeneity as well as comparison of quantified ER, PR and HER2 results. Results: Ki67 is often used as biomarker to distinguish Luminal A from Luminal B subgroups. The concordance between MammaPrint and centrally assessed Ki67 in Luminal-type patients is 71%, with a κ score of 0.35 (95% CI 0.26–0.45) indicating that Ki67 and MammaPrint cannot reliably substitute for each other. There is a relatively large group of clinical HER2+ cases that are BluePrint Luminal-type (29 out of 76; 38%) indicating that tumor expression of the Luminal profile is dominant compared with expression of the HER2 profile. These patients have high IHC ER results and fall into the group that St Gallen separately defines as Luminal B HER2-type. These patients may have lower response to trastuzumab [von Minckwitz et al. JCO 2012]. 12 out of 76 BluePrint Basal-type patients are clinical HR+. These patients have low centrally assessed IHC ER and PR expression (≥1% and Conclusions: Marked differences are observed between BluePrint and MammaPrint (microarray based) breast cancer subtypes and centrally re-assessed pathological surrogates (based on ER, PR, HER2 & Ki67). The greatest discordance is seen in the sub-stratification of Luminal patients, and in the HR+/HER2+ patients. The observed subtype discrepancies may have an important impact on treatment decision making. Citation Information: Cancer Res 2012;72(24 Suppl):Abstract nr P3-05-02.