Association Between Results of a Gene Expression Signature Assay and Recurrence-Free Interval in Patients With Stage II Colon Cancer in Cancer and Leukemia Group B 9581 (Alliance)

PURPOSE: Conventional staging methods are inadequate to identify patients with stage II colon cancer (CC) who are at high risk of recurrence after surgery with curative intent. ColDx is a gene expression, microarray-based assay shown to be independently prognostic for recurrence-free interval (RFI) and overall survival in CC. The objective of this study was to further validate ColDx using formalin-fixed, paraffin-embedded specimens collected as part of the Alliance phase III trial, C9581.

PATIENTS AND METHODS: C9581 evaluated edrecolomab versus observation in patients with stage II CC and reported no survival benefit. Under an initial case-cohort sampling design, a randomly selected subcohort (RS) comprised 514 patients from 901 eligible patients with available tissue. Forty-nine additional patients with recurrence events were included in the analysis. Final analysis comprised 393 patients: 360 RS (58 events) and 33 non-RS events. Risk status was determined for each patient by ColDx. The Self-Prentice method was used to test the association between the resulting ColDx risk score and RFI adjusting for standard prognostic variables.

RESULTS: Fifty-five percent of patients (216 of 393) were classified as high risk. After adjustment for prognostic variables that included mismatch repair (MMR) deficiency, ColDx high-risk patients exhibited significantly worse RFI (multivariable hazard ratio, 2.13; 95% CI, 1.3 to 3.5; P < .01). Age and MMR status were marginally significant. RFI at 5 years for patients classified as high risk was 82% (95% CI, 79% to 85%), compared with 91% (95% CI, 89% to 93%) for patients classified as low risk.

CONCLUSION: ColDx is associated with RFI in the C9581 subsample in the presence of other prognostic factors, including MMR deficiency. ColDx could be incorporated with the traditional clinical markers of risk to refine patient prognosis.

Improved clonality assessment in germinal centre/post-germinal centre non-Hodgkin's lymphomas with high rates of somatic hypermutation.

BACKGROUND: PCR detects clonal rearrangements of the Ig gene in lymphoproliferative disorders. False negativity occurs in germinal centre/post-germinal centre lymphomas (GC/PGCLs) as they display a high rate of somatic hypermutation (SHM), which causes primer mismatching when detecting Ig rearrangements by PCR. AIMS: To investigate the degree of SHM in a group of GC/PGCLs and assess the rate of false negativity when using BIOMED-2 PCR when compared with previously published strategies. METHODS: DNA was isolated from snap-frozen tissue from 49 patients with GC/PGCL (23 diffuse large B cell lymphomas (DLBCLs), 26 follicular lymphomas (FLs)) and PCR-amplified for complete (VDJH), incomplete (DJH) and Ig kappa/lambda rearrangements using the BIOMED-2 protocols, and compared with previously published methods using consensus primers. Germinal centre phenotype was defined by immunohistochemistry based on CD10, Bcl-6 and MUM-1. RESULTS: Clonality detection by amplifying Ig rearrangements using BIOMED-2 family-specific primers was considerably higher than that found using consensus primers (74% DLBCL and 96% FL vs 69% DLBCL and 73% FL). Addition of BIOMED-2 DJH rearrangements increased detection of clonality by 22% in DLBCL. SHM was present in VDJH rearrangements from all patients with DLBCL (median (range) 5.7% (2.5-13.5)) and FL (median (range) 5.3% (2.3-11.9)) with a clonal rearrangement. CONCLUSIONS: Use of BIOMED-2 primers has significantly reduced the false negative rate associated with GC/PGCL when compared with consensus primers, and the inclusion of DJH rearrangements represents a potential complementary target for clonality assessment, as SHM is thought not to occur in these types of rearrangements.

Deep Corals, Deep Learning: Moving the Deep Net Towards Real-Time Image Annotation

The mismatch between human capacity and the acquisition of Big Data such as Earth imagery undermines commitments to Convention on Biological Diversity (CBD) and Aichi targets. Artificial intelligence (AI) solutions to Big Data issues are urgently needed as these could prove to be faster, more accurate, and cheaper. Reducing costs of managing protected areas in remote deep waters and in the High Seas is of great importance, and this is a realm where autonomous technology will be transformative.