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PTEN and DNA Ploidy Status by Machine Learning in Prostate Cancer.

ABSTRACT: Machine learning (ML) is expected to improve biomarker assessment. Using convolution neural networks, we developed a fully-automated method for assessing PTEN protein status in immunohistochemically-stained slides using a radical prostatectomy (RP) cohort (n = 253). It was validated according to a predefined protocol in an independent RP cohort (n = 259), alone and by measuring its prognostic value in combination with DNA ploidy status determined by ML-based image cytometry. In the primary analysis, automatically assessed dichotomized PTEN status was associated with time to biochemical recurrence (TTBCR) (hazard ratio (HR) = 3.32, 95% CI 2.05 to 5.38). Patients with both non-diploid tumors and PTEN-low had an HR of 4.63 (95% CI 2.50 to 8.57), while patients with one of these characteristics had an HR of 1.94 (95% CI 1.15 to 3.30), compared to patients with diploid tumors and PTEN-high, in univariable analysis of TTBCR in the validation cohort. Automatic PTEN scoring was strongly predictive of the PTEN status assessed by human experts (area under the curve 0.987 (95% CI 0.968 to 0.994)). This suggests that PTEN status can be accurately assessed using ML, and that the combined marker of automatically assessed PTEN and DNA ploidy status may provide an objective supplement to the existing risk stratification factors in prostate cancer.

SUBMITTER: Cyll K

PROVIDER: S-EPMC8428363 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundCell-free DNA's (cfDNA) use as a biomarker in cancer is challenging due to genetic heterogeneity of malignancies and rarity of tumor-derived molecules. Here we describe and demonstrate a novel machine-learning guided panel design strategy for improving the detection of tumor variants in cfDNA. Using this approach, we first generated a model to classify and score candidate variants for inclusion on a prostate cancer targeted sequencing panel. We then used this panel to screen tumor variants from prostate cancer patients with localized disease in both in silico and hybrid capture settings.MethodsWhole Genome Sequence (WGS) data from 550 prostate tumors was analyzed to build a targeted sequencing panel of single point and small (< 200 bp) indel mutations, which was subsequently screened in silico against prostate tumor sequences from 5 patients to assess performance against commonly used alternative panel designs. The panel's ability to detect tumor-derived cfDNA variants was then assessed using prospectively collected cfDNA and tumor foci from a test set 18 prostate cancer patients with localized disease undergoing radical proctectomy.ResultsThe panel generated from this approach identified as top candidates mutations in known driver genes (e.g. HRAS) and prostate cancer related transcription factor binding sites (e.g. MYC, AR). It outperformed two commonly used designs in detecting somatic mutations found in the cfDNA of 5 prostate cancer patients when analyzed in an in silico setting. Additionally, hybrid capture and 2500X sequencing of cfDNA molecules using the panel resulted in detection of tumor variants in all 18 patients of a test set, where 15 of the 18 patients had detected variants found in multiple foci.ConclusionMachine learning-prioritized targeted sequencing panels may prove useful for broad and sensitive variant detection in the cfDNA of heterogeneous diseases. This strategy has implications for disease detection and monitoring when applied to the cfDNA isolated from prostate cancer patients.

Project description:We have previously demonstrated a significant correlative relationship between PTEN deletion and ERG rearrangement, both in the development of clinically localized prostate cancers and metastases. Herein, we evaluate the cooperative role of ERG and PTEN in oncological outcomes after radical prostatectomy for clinically localized prostate cancer. We evaluated ERG and PTEN status using three previously described cohorts. The first cohort included 235 clinically localized prostate cancer cases represented on tissue microarrays (TMA), evaluated using previously validated FISH assays for ERG and PTEN. The second cohort included 167 cases of clinically localized prostate cancer on TMAs evaluated for PTEN by FISH, and for PTEN and ERG by dual IHC. The third cohort comprised 59 clinically localized prostate cancer cases assessed by array comparative genomic hybridization (aCGH). Kaplan-Meir plots and long rank tests were used to assess the association of ERG and PTEN status with biochemical recurrence after radical prostatectomy for clinically localized prostate cancer. Of the 317 cases eligible for analyses with evaluable ERG and PTEN status, 88 (27.8%) patients developed biochemical recurrence over a median follow-up of 5.7 years. Overall, 45% (142/317) of cases demonstrated ERG rearrangement and 20% (62/317) of cases demonstrated PTEN loss. Hemizygous and homozygous deletion of PTEN was seen in 10% (18/175) and 3% (5/175) of ERG-negative cases, respectively. In contrast, hemizygous and homozygous deletion of PTEN was seen in 11% (15/142) and 17% (24/123) of ERG-positive cases, respectively. PTEN loss (heterozygous or homozygous) was significantly associated with shorter time to biochemical recurrence compared to no PTEN loss (p < 0.001). However, ERG rearrangement versus no rearrangement was not associated with time to PSA recurrence (p = 0.15). Patients who exhibited ERG rearrangement and loss of PTEN had no significant difference in time to recurrence compared to patients with wild-type ERG and loss of PTEN (p = 0.30). Our findings confirm a mutual cooperative role of ERG and PTEN in the pathogenesis of prostate cancer, particularly for homozygous PTEN deletion. ERG did not stratify outcome either alone or in combination with PTEN in this cohort.

Project description:Prostate cancer (PCa) is the most frequent male malignancy and the assessment of PCa aggressiveness, for which a biopsy is required, is fundamental for patient management. Currently, multiparametric (mp) MRI is strongly recommended before biopsy. Quantitative assessment of mpMRI might provide the radiologist with an objective and noninvasive tool for supporting the decision-making in clinical practice and decreasing intra- and inter-reader variability. In this view, high dimensional radiomics features and Machine Learning (ML) techniques, along with Deep Learning (DL) methods working on raw images directly, could assist the radiologist in the clinical workflow. The aim of this study was to develop and validate ML/DL frameworks on mpMRI data to characterize PCas according to their aggressiveness. We optimized several ML/DL frameworks on T2w, ADC and T2w+ADC data, using a patient-based nested validation scheme. The dataset was composed of 112 patients (132 peripheral lesions with Prostate Imaging Reporting and Data System (PI-RADS) score ≥ 3) acquired following both PI-RADS 2.0 and 2.1 guidelines. Firstly, ML/DL frameworks trained and validated on PI-RADS 2.0 data were tested on both PI-RADS 2.0 and 2.1 data. Then, we trained, validated and tested ML/DL frameworks on a multi PI-RADS dataset. We reported the performances in terms of Area Under the Receiver Operating curve (AUROC), specificity and sensitivity. The ML/DL frameworks trained on T2w data achieved the overall best performance. Notably, ML and DL frameworks trained and validated on PI-RADS 2.0 data obtained median AUROC values equal to 0.750 and 0.875, respectively, on unseen PI-RADS 2.0 test set. Similarly, ML/DL frameworks trained and validated on multi PI-RADS T2w data showed median AUROC values equal to 0.795 and 0.750, respectively, on unseen multi PI-RADS test set. Conversely, all the ML/DL frameworks trained and validated on PI-RADS 2.0 data, achieved AUROC values no better than the chance level when tested on PI-RADS 2.1 data. Both ML/DL techniques applied on mpMRI seem to be a valid aid in predicting PCa aggressiveness. In particular, ML/DL frameworks fed with T2w images data (objective, fast and non-invasive) show good performances and might support decision-making in patient diagnostic and therapeutic management, reducing intra- and inter-reader variability.

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PTEN and DNA Ploidy Status by Machine Learning in Prostate Cancer.

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