State-transition modeling of blood transcriptome predicts disease evolution and treatment response in chronic myeloid leukemia.

Chronic myeloid leukemia (CML) is initiated and maintained by BCR::ABL which is clinically targeted using tyrosine kinase inhibitors (TKIs). TKIs can induce long-term remission but are also not curative. Thus, CML is an ideal system to test our hypothesis that transcriptome-based state-transition models accurately predict cancer evolution and treatment response. We collected time-sequential blood samples from tetracycline-off (Tet-Off) BCR::ABL-inducible transgenic mice and wild-type controls. From the transcriptome, we constructed a CML state-space and a three-well leukemogenic potential landscape. The potential's stable critical points defined observable disease states. Early states were characterized by anti-CML genes opposing leukemia; late states were characterized by pro-CML genes. Genes with expression patterns shaped similarly to the potential landscape were identified as drivers of disease transition. Re-introduction of tetracycline to silence the BCR::ABL gene returned diseased mice transcriptomes to a near healthy state, without reaching it, suggesting parts of the transition are irreversible. TKI only reverted the transcriptome to an intermediate disease state, without approaching a state of health; disease relapse occurred soon after treatment. Using only the earliest time-point as initial conditions, our state-transition models accurately predicted both disease progression and treatment response, supporting this as a potentially valuable approach to time clinical intervention, before phenotypic changes become detectable.

State-transition Modeling of Blood Transcriptome Predicts Disease Evolution and Treatment Response in Chronic Myeloid Leukemia.

Chronic myeloid leukemia (CML) is initiated and maintained by BCR::ABL which is clinically targeted using tyrosine kinase inhibitors (TKIs). TKIs can induce long-term remission but are also not curative. Thus, CML is an ideal system to test our hypothesis that transcriptome-based state-transition models accurately predict cancer evolution and treatment response. We collected time-sequential blood samples from tetracycline-off (Tet-Off) BCR::ABL-inducible transgenic mice and wild-type controls. From the transcriptome, we constructed a CML state-space and a three-well leukemogenic potential landscape. The potential's stable critical points defined observable disease states. Early states were characterized by anti-CML genes opposing leukemia; late states were characterized by pro-CML genes. Genes with expression patterns shaped similarly to the potential landscape were identified as drivers of disease transition. Re-introduction of tetracycline to silence the BCR::ABL gene returned diseased mice transcriptomes to a near healthy state, without reaching it, suggesting parts of the transition are irreversible. TKI only reverted the transcriptome to an intermediate disease state, without approaching a state of health; disease relapse occurred soon after treatment. Using only the earliest time-point as initial conditions, our state-transition models accurately predicted both disease progression and treatment response, supporting this as a potentially valuable approach to time clinical intervention even before phenotypic changes become detectable.

Comparison against 186 canid whole-genome sequences reveals survival strategies of an ancient clonally transmissible canine tumor.

Canine transmissible venereal tumor (CTVT) is a parasitic cancer clone that has propagated for thousands of years via sexual transfer of malignant cells. Little is understood about the mechanisms that converted an ancient tumor into the world's oldest known continuously propagating somatic cell lineage. We created the largest existing catalog of canine genome-wide variation and compared it against two CTVT genome sequences, thereby separating alleles derived from the founder's genome from somatic mutations that must drive clonal transmissibility. We show that CTVT has undergone continuous adaptation to its transmissible allograft niche, with overlapping mutations at every step of immunosurveillance, particularly self-antigen presentation and apoptosis. We also identified chronologically early somatic mutations in oncogenesis- and immune-related genes that may represent key initiators of clonal transmissibility. Thus, we provide the first insights into the specific genomic aberrations that underlie CTVT's dogged perseverance in canids around the world.

Potential impact of adding genetic markers to clinical parameters in predicting prostate biopsy outcomes in men following an initial negative biopsy: findings from the REDUCE trial.

BACKGROUND: Several germline single nucleotide polymorphisms (SNPs) have been consistently associated with prostate cancer (PCa) risk. OBJECTIVE: To determine whether there is an improvement in PCa risk prediction by adding these SNPs to existing predictors of PCa. DESIGN, SETTING, AND PARTICIPANTS: Subjects included men in the placebo arm of the randomized Reduction by Dutasteride of Prostate Cancer Events (REDUCE) trial in whom germline DNA was available. All men had an initial negative prostate biopsy and underwent study-mandated biopsies at 2 yr and 4 yr. Predictive performance of baseline clinical parameters and/or a genetic score based on 33 established PCa risk-associated SNPs was evaluated. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: Area under the receiver operating characteristic curves (AUC) were used to compare different models with different predictors. Net reclassification improvement (NRI) and decision curve analysis (DCA) were used to assess changes in risk prediction by adding genetic markers. RESULTS AND LIMITATIONS: Among 1654 men, genetic score was a significant predictor of positive biopsy, even after adjusting for known clinical variables and family history (p = 3.41 × 10(-8)). The AUC for the genetic score exceeded that of any other PCa predictor at 0.59. Adding the genetic score to the best clinical model improved the AUC from 0.62 to 0.66 (p<0.001), reclassified PCa risk in 33% of men (NRI: 0.10; p=0.002), resulted in higher net benefit from DCA, and decreased the number of biopsies needed to detect the same number of PCa instances. The benefit of adding the genetic score was greatest among men at intermediate risk (25th percentile to 75th percentile). Similar results were found for high-grade (Gleason score ≥ 7) PCa. A major limitation of this study was its focus on white patients only. CONCLUSIONS: Adding genetic markers to current clinical parameters may improve PCa risk prediction. The improvement is modest but may be helpful for better determining the need for repeat prostate biopsy. The clinical impact of these results requires further study.

Validation of TPX2 as a potential therapeutic target in pancreatic cancer cells.

PURPOSE: The targeting protein for Xklp2 (TPX2) has recently gained attention as a putative oncogene possibly amplified in several human malignancies, including pancreatic adenocarcinoma. In this work, we sought to evaluate the copy number and expression of TPX2 in pancreatic cancer cell lines and tumor tissues and to further explore the potential of TPX2 as a therapeutic target. EXPERIMENTAL DESIGN: The DNA copy number and expression of the TPX2 gene were surveyed in pancreatic cancer cell lines and tumor tissues and compared with those of immortalized normal pancreatic ductal cells and normal pancreatic tissues. The cellular effects of TPX2 knockdown using small interfering RNA oligonucleotides in pancreatic cancer cells, such as growth in tissue culture, in soft agar, and in nude mice; apoptosis; and sensitivity to paclitaxel, were also investigated using various assays. RESULTS: Low-copy-number TPX2 amplification was found in pancreatic cancer cell lines and low-passage pancreatic cancer tumor xenografts. TPX2 expression was upregulated in pancreatic cancer cell lines at both the mRNA and protein levels relative to the immortalized pancreatic ductal epithelial cell line HPDE6. Immunohistochemical staining of a tissue microarray showed that TPX2 expression was higher in pancreatic tumors compared with their normal counterparts. Treatment with TPX2 targeting small interfering RNAs effectively reduced pancreatic cancer cell growth in tissue culture, induced apoptosis, and inhibited growth in soft agar and in nude mice. Knockdown of TPX2 also sensitized pancreatic cancer cells to paclitaxel treatment. CONCLUSIONS: Our results suggest that TPX2 might be an attractive target for pancreatic cancer therapy.

Germline copy number polymorphisms involving larger than 100 kb are uncommon in normal subjects.

BACKGROUND: Recent studies using ROMA and Array-CGH suggest that germline copy number polymorphisms (CNPs) involving >100 kb are common in humans. METHODS: In this study, we used the Affymetrix GeneChip 100K single nucleotide polymorphisms (SNP) mapping panel to further examine the type and frequency of germline CNPs in the genome. By utilizing the allele intensity data generated while genotyping approximately 116,000 SNPs among 23 subjects from 4 families, we were able to detect multiple CNPs. RESULTS: However, in contrast to several previous studies, we found that CNPs >100 kb are rare in the genome but CNPs involving 100s-1,000s of base pairs are more common. CONCLUSIONS: We have demonstrated the utility of this approach, which has an important advantage over other methods because it is able to simultaneously assess both CNPs and SNPs, and therefore has great potential in genetic association studies of common diseases.

Ratio statistics of gene expression levels and applications to microarray data analysis.

MOTIVATION: Expression-based analysis for large families of genes has recently become possible owing to the development of cDNA microarrays, which allow simultaneous measurement of transcript levels for thousands of genes. For each spot on a microarray, signals in two channels must be extracted from their backgrounds. This requires algorithms to extract signals arising from tagged mRNA hybridized to arrayed cDNA locations and algorithms to determine the significance of signal ratios. RESULTS: This paper focuses on estimation of signal ratios from the two channels, and the significance of those ratios. The key issue is the determination of whether a ratio is significantly high or low in order to conclude whether the gene is upregulated or downregulated. The paper builds on an earlier study that involved a hypothesis test based on a ratio statistic under the supposition that the measured fluorescent intensities subsequent to image processing can be assumed to reflect the signal intensities. Here, a refined hypothesis test is considered in which the measured intensities forming the ratio are assumed to be combinations of signal and background. The new method involves a signal-to-noise ratio, and for a high signal-to-noise ratio the new test reduces (with close approximation) to the original test. The effect of low signal-to-noise ratio on the ratio statistics constitutes the main theme of the paper. Finally, and in this vein, a quality metric is formulated for spots. This measure can be used to decide whether or not a spot ratio should be deleted, or to adjust various measurements to reflect confidence in the quality of the measurement. CONTACT: e-dougherty@tamu.edu