The significance of tumor heterogeneity for prediction of DNA ploidy of prostate cancer

Exploring the intratumoral heterogeneity of DNA ploidy in prostate cancer

BACKGROUND

Prostate cancer is morphologically and molecularly heterogeneous. Genomic heterogeneity might be mirrored by variability in DNA ploidy. Aneuploidy is a hallmark of genomic instability and associated with tumor aggressiveness. Little attention has been paid to the biological significance of the diploid tumor cell population that often coexists with aneuploid populations. Here, we investigated the role of DNA ploidy in tumor heterogeneity and clonal evolution.

METHODS

Three radical prostatectomy specimens with intratumoral heterogeneity based on nuclear features on H&E were selected. DNA content of each subpopulation was determined by DNA image cytometry and silver in situ hybridization (SISH). Genomic evolution was inferred from array comparative genomic hybridization (aCGH). Additionally, immunohistochemistry was used to examine the stemness-associated marker ALDH1A1.

RESULTS

Nuclear morphology reliably predicted DNA ploidy status in all three cases. In one case, aCGH analysis revealed several shared deletions and one amplification in both the diploid and the aneuploid population, suggesting that these populations could be related. In the other two cases, a statement about relatedness was not possible. Furthermore, ALDH1A1 was expressed in 2/3 cases and exclusively observed in their diploid populations.

CONCLUSIONS

In this proof-of-concept study, we demonstrate the feasibility to predict the DNA ploidy status of distinct populations within one tumor by H&E morphology. Future studies are needed to further investigate the clonal relationship between the diploid and the aneuploid subpopulation and test the hypothesis that the aneuploid population is derived from the diploid one. Finally, our analyses pointed to an enrichment of the stemness-associated marker ALDH1A1 in diploid populations, which warrants further investigation in future studies.

DNA ploidy measured on archived pretreatment biopsy material may correlate with prostate-specific antigen recurrence after prostate brachytherapy

PURPOSE

To explore whether DNA ploidy of prostate cancer cells determined from archived transrectal ultrasound-guided biopsy specimens correlates with disease-free survival.

METHODS AND MATERIALS

Forty-seven failures and 47 controls were selected from 1006 consecutive low- and intermediate-risk patients treated with prostate (125)I brachytherapy (July 1998-October 2003). Median follow-up was 7.5 years. Ten-year actuarial disease-free survival was 94.1%. Controls were matched using age, initial prostate-specific antigen level, clinical stage, Gleason score, use of hormone therapy, and follow-up (all P nonsignificant). Seventy-eight specimens were successfully processed; 27 control and 20 failure specimens contained more than 100 tumor cells were used for the final analysis. The Feulgen-Thionin stained cytology samples from archived paraffin blocks were used to determine the DNA ploidy of each tumor by measuring integrated optical densities.

RESULTS

The samples were divided into diploid and aneuploid tumors. Aneuploid tumors were found in 16 of 20 of the failures (80%) and 8 of 27 controls (30%). Diploid DNA patients had a significantly lower rate of disease recurrence (P=.0086) (hazard ratio [HR] 0.256). On multivariable analysis, patients with aneuploid tumors had a higher prostate-specific antigen failure rate (HR 5.13). Additionally, those with "excellent" dosimetry (V100 >90%; D90 >144 Gy) had a significantly lower recurrence rate (HR 0.25). All patients with aneuploid tumors and dosimetry classified as "nonexcellent" (V100 <90%; D90 <144 Gy) (5 of 5) had disease recurrence, compared with 40% of patients with aneuploid tumors and "excellent" dosimetry (8 of 15). In contrast, dosimetry did not affect the outcome for diploid patients.

CONCLUSIONS

Using core biopsy material from archived paraffin blocks, DNA ploidy correctly classified the majority of failures and nonfailures in this study. The results suggest that DNA ploidy can be used as a useful marker for aggressiveness of localized prostate cancer. A larger study will be necessary to further confirm our hypothesis.

Intratumor heterogeneity in evolutionary models of tumor progression

With rare exceptions, human tumors arise from single cells that have accumulated the necessary number and types of heritable alterations. Each such cell leads to dysregulated growth and eventually the formation of a tumor. Despite their monoclonal origin, at the time of diagnosis most tumors show a striking amount of intratumor heterogeneity in all measurable phenotypes; such heterogeneity has implications for diagnosis, treatment efficacy, and the identification of drug targets. An understanding of the extent and evolution of intratumor heterogeneity is therefore of direct clinical importance. In this article, we investigate the evolutionary dynamics of heterogeneity arising during exponential expansion of a tumor cell population, in which heritable alterations confer random fitness changes to cells. We obtain analytical estimates for the extent of heterogeneity and quantify the effects of system parameters on this tumor trait. Our work contributes to a mathematical understanding of intratumor heterogeneity and is also applicable to organisms like bacteria, agricultural pests, and other microbes.

A Hidden Markov Model to estimate population mixture and allelic copy-numbers in cancers using Affymetrix SNP arrays

Background

Affymetrix SNP arrays can interrogate thousands of SNPs at the same time. This allows us to look at the genomic content of cancer cells and to investigate the underlying events leading to cancer. Genomic copy-numbers are today routinely derived from SNP array data, but the proposed algorithms for this task most often disregard the genotype information available from germline cells in paired germline-tumour samples. Including this information may deepen our understanding of the "true" biological situation e.g. by enabling analysis of allele specific copy-numbers. Here we rely on matched germline-tumour samples and have developed a Hidden Markov Model (HMM) to estimate allelic copy-number changes in tumour cells. Further with this approach we are able to estimate the proportion of normal cells in the tumour (mixture proportion).

Results

We show that our method is able to recover the underlying copy-number changes in simulated data sets with high accuracy (above 97.71%). Moreover, although the known copy-numbers could be well recovered in simulated cancer samples with more than 70% cancer cells (and less than 30% normal cells), we demonstrate that including the mixture proportion in the HMM increases the accuracy of the method. Finally, the method is tested on HapMap samples and on bladder and prostate cancer samples.

Conclusion

The HMM method developed here uses the genotype calls of germline DNA and the allelic SNP intensities from the tumour DNA to estimate allelic copy-numbers (including changes) in the tumour. It differentiates between different events like uniparental disomy and allelic imbalances. Moreover, the HMM can estimate the mixture proportion, and thus inform about the purity of the tumour sample.

Identification of patients with low-risk for aneuploidy: comparative discriminatory models using linear and machine-learning classifiers in prostate cancer

BACKGROUND

Prostate needle biopsy (PNB) ploidy status has proven utility to predict adverse outcomes after prostatectomy. We sought to develop models to predict ploidy status using clinicopathologic variables.

METHODS

We identified a cohort of 169 patients with a diagnosis of prostatic adenocarcinoma on PNB, and estimated ploidy status (determined using Feulgen stained biopsy tissue) using four predictors, including age, prebiopsy PSA, highest Gleason score (GS), and the percentage of involvement by carcinoma at the biopsy site with the highest GS (PCARBX). Logistic regression (LR), Neural Network (NN), and CART classifiers were constructed.

RESULTS

Univariate analyses revealed all four predictors to be significantly associated with ploidy status. On multivariable analyses, LR identified a 2-parameter model, including GS and PCARBX that had a significant ability to predict ploidy status with a 74% and 75% correct classification rate (CCR), respectively. Using the same variables, CART and NN yielded similar CCRs of 70.4%. Within GS = 6 cohort, the CART model classified over 90% of biopsies as diploid when patients had a PCARBX < 55% and a log(PSA) < 1.7.

CONCLUSIONS

Our study demonstrates that models using GS and PCARBX are able to predict PNB ploidy status with acceptable accuracy. While machine learning classifier-derived models yield similar accuracy as LR-derived models, the latter methodology has the distinct advantage of being applicable in future datasets to estimate case-specific predictions. This information may be useful in identifying potentially aneuploid patients, who can then be targeted for more aggressive therapy.

The 20-Yr Outcome in Patients with Well- or Moderately Differentiated Clinically Localized Prostate Cancer Diagnosed in the Pre-PSA Era: The Prognostic Value of Tumour Ploidy and Comorbidity

OBJECTIVE

This observational cohort study describes the long-term outcome of patients with clinically localized prostate cancer managed with watchful waiting, the prognostic value of tumour ploidy, and the impact of comorbidity.

METHODS

A total of 119 patients with clinically localized (T1-2) prostate cancer consecutively diagnosed from 1978 to 1982 were prospectively managed by watchful waiting, with treatment given if progression occurred.

RESULTS

Median age was 68 yr. Median observation time was 24 yr+/-6.25 (SD). Of the 112 patients who died, 42 died of prostate cancer. Disease-specific survival rates were 85% (95% CI: 77-93%), 58% (46-70%), and 32% (19-46%) at 10, 15, and 20 yr, respectively. Treatment-free survival rate was 43% (95% CI: 33-54%) at 10 yr. Patients aged 70 yr and over had a statistically significant increased risk of dying from any cause. There was a statistically significant increased risk of dying from prostate cancer for patients with nondiploid tumours.

CONCLUSION

In the present series from the pre-PSA era, watchful waiting yielded a relatively high long-term disease-specific survival rate in patients with well- or moderately differentiated clinically localized prostate cancer, and almost half were not treated 10 yr after diagnosis. Watchful waiting may be an option at least for such patients with a 10- to 15-yr life expectancy. Age of 70 yr or more predicted an increased overall mortality. High comorbidity increased the risk (although not statistically significant) for death from any cause and for death from prostate cancer. Patients with nondiploid tumours were at an increased risk to die from prostate cancer.