Peripheral blood marker of residual acute leukemia after hematopoietic cell transplantation using multi-plex digital droplet PCR

Background

Relapse remains the primary cause of death after hematopoietic cell transplantation (HCT) for acute leukemia. The ability to identify minimal/measurable residual disease (MRD) via the blood could identify patients earlier when immunologic interventions may be more successful. We evaluated a new test that could quantify blood tumor mRNA as leukemia MRD surveillance using droplet digital PCR (ddPCR).

Methods

The multiplex ddPCR assay was developed using tumor cell lines positive for the tumor associated antigens (TAA: WT1, PRAME, BIRC5), with homeostatic ABL1. On IRB-approved protocols, RNA was isolated from mononuclear cells from acute leukemia patients after HCT (n = 31 subjects; n = 91 specimens) and healthy donors (n = 20). ddPCR simultaneously quantitated mRNA expression of WT1, PRAME, BIRC5, and ABL1 and the TAA/ABL1 blood ratio was measured in patients with and without active leukemia after HCT.

Results

Tumor cell lines confirmed quantitation of TAAs. In patients with active acute leukemia after HCT (MRD+ or relapse; n=19), the blood levels of WT1/ABL1, PRAME/ABL1, and BIRC5/ABL1 exceeded healthy donors (p<0.0001, p=0.0286, and p=0.0064 respectively). Active disease status was associated with TAA positivity (1+ TAA vs 0 TAA) with an odds ratio=10.67, (p=0.0070, 95% confidence interval 1.91 - 59.62). The area under the curve is 0.7544. Changes in ddPCR correlated with disease response captured on standard of care tests, accurately denoting positive or negative disease burden in 15/16 (95%). Of patients with MRD+ or relapsed leukemia after HCT, 84% were positive for at least one TAA/ABL1 in the peripheral blood. In summary, we have developed a new method for blood MRD monitoring of leukemia after HCT and present preliminary data that the TAA/ABL1 ratio may may serve as a novel surrogate biomarker for relapse of acute leukemia after HCT.

Blur identification from vector quantizer encoder distortion

Blur identification is a crucial first step in many image restoration techniques. An approach for identifying image blur using vector quantizer encoder distortion is proposed. The blur in an image is identified by choosing from a finite set of candidate blur functions. The method requires a set of training images produced by each of the blur candidates. Each of these sets is used to train a vector quantizer codebook. Given an image degraded by unknown blur, it is first encoded with each of these codebooks. The blur in the image is then estimated by choosing from among the candidates, the one corresponding to the codebook that provides the lowest encoder distortion. Simulations are performed at various bit rates and with different levels of noise. Results show that the method performs well even at a signal-to-noise ratio (SNR) as low as 10 dB.

Lapped nonlinear interpolative vector quantization and image super-resolution

This correspondence presents an improved version of an algorithm designed to perform image restoration via nonlinear interpolative vector quantization (NLIVQ). The improvement results from using lapped blocks during the decoding process. The algorithm is trained on original and diffraction-limited image pairs. The discrete cosine transform is again used in the codebook design process to control complexity. Simulation results are presented which demonstrate improvements over the nonlapped algorithm in both observed image quality and peak signal-to-noise ratio. In addition, the nonlinearity of the algorithm is shown to produce super-resolution in the restored images.