Multimodal Biomarkers That Predict the Presence of Gleason Pattern 4: Potential Impact for Active Surveillance.

PURPOSE: Latent grade group ≥2 prostate cancer can impact the performance of active surveillance protocols. To date, molecular biomarkers for active surveillance have relied solely on RNA or protein. We trained and independently validated multimodal (mRNA abundance, DNA methylation, and/or DNA copy number) biomarkers that more accurately separate grade group 1 from grade group ≥2 cancers. MATERIALS AND METHODS: Low- and intermediate-risk prostate cancer patients were assigned to training (n=333) and validation (n=202) cohorts. We profiled the abundance of 342 mRNAs, 100 DNA copy number alteration loci, and 14 hypermethylation sites at 2 locations per tumor. Using the training cohort with cross-validation, we evaluated methods for training classifiers of pathological grade group ≥2 in centrally reviewed radical prostatectomies. We trained 2 distinct classifiers, PRONTO-e and PRONTO-m, and validated them in an independent radical prostatectomy cohort. RESULTS: PRONTO-e comprises 353 mRNA and copy number alteration features. PRONTO-m includes 94 clinical, mRNAs, copy number alterations, and methylation features at 14 and 12 loci, respectively. In independent validation, PRONTO-e and PRONTO-m predicted grade group ≥2 with respective true-positive rates of 0.81 and 0.76, and false-positive rates of 0.43 and 0.26. Both classifiers were resistant to sampling error and identified more upgrading cases than a well-validated presurgical risk calculator, CAPRA (Cancer of the Prostate Risk Assessment; P < .001). CONCLUSIONS: Two grade group classifiers with superior accuracy were developed by incorporating RNA and DNA features and validated in an independent cohort. Upon further validation in biopsy samples, classifiers with these performance characteristics could refine selection of men for active surveillance, extending their treatment-free survival and intervals between surveillance.

Genomic characterization of HER2-positive breast cancer and response to neoadjuvant trastuzumab and chemotherapy-results from the ACOSOG Z1041 (Alliance) trial.

Background: HER2 (ERBB2) gene amplification and its corresponding overexpression are present in 15-30% of invasive breast cancers. While HER2-targeted agents are effective treatments, resistance remains a major cause of death. The American College of Surgeons Oncology Group Z1041 trial (NCT00513292) was designed to compare the pathologic complete response (pCR) rate of distinct regimens of neoadjuvant chemotherapy and trastuzumab, but ultimately identified no difference. Patients and methods: In supplement to tissues from 37 Z1041 cases, 11 similarly treated cases were obtained from a single institution study (NCT00353483). We have extracted genomic DNA from both pre-treatment tumor biopsies and blood of these 48 cases, and performed whole genome (WGS) and exome sequencing. Coincident with these efforts, we have generated RNA-seq profiles from 42 of the tumor biopsies. Among patients in this cohort, 24 (50%) achieved a pCR. Results: We have characterized the genomic landscape of HER2-positive breast cancer and investigated associations between genomic features and pCR. Cases assigned to the HER2-enriched subtype by RNA-seq analysis were more likely to achieve a pCR compared to the luminal, basal-like, or normal-like subtypes (19/27 versus 3/15; P = 0.0032). Mutational events led to the generation of putatively active neoantigens, but were overall not associated with pCR. ERBB2 and GRB7 were the genes most commonly observed in fusion events, and genomic copy number analysis of the ERBB2 locus indicated that cases with either no observable or low-level ERBB2 amplification were less likely to achieve a pCR (7/8 versus 17/40; P = 0.048). Moreover, among cases that achieved a pCR, tumors consistently expressed immune signatures that may contribute to therapeutic response. Conclusion: The identification of these features suggests that it may be possible to predict, at the time of diagnosis, those HER2-positive breast cancer patients who will not respond to treatment with chemotherapy and trastuzumab. ClinicalTrials.gov identifiers: NCT00513292, NCT00353483.

Detecting gene signature activation in breast cancer in an absolute, single-patient manner.

BACKGROUND: The ability to reliably identify the state (activated, repressed, or latent) of any molecular process in the tumor of a patient from an individual whole-genome gene expression profile obtained from microarray or RNA sequencing (RNA-seq) promises important clinical utility. Unfortunately, all previous bioinformatics tools are only applicable in large and diverse panels of patients, or are limited to a single specific pathway/process (e.g. proliferation). METHODS: Using a panel of 4510 whole-genome gene expression profiles from 10 different studies we built and selected models predicting the activation status of a compendium of 1733 different biological processes. Using a second independent validation dataset of 742 patients we validated the final list of 1773 models to be included in a de novo tool entitled absolute inference of patient signatures (AIPS). We also evaluated the prognostic significance of the 1773 individual models to predict outcome in all and in specific breast cancer subtypes. RESULTS: We described the development of the de novo tool entitled AIPS that can identify the activation status of a panel of 1733 different biological processes from an individual breast cancer microarray or RNA-seq profile without recourse to a broad cohort of patients. We demonstrated that AIPS is stable compared to previous tools, as the inferred pathway state is not affected by the composition of a dataset. We also showed that pathway states inferred by AIPS are in agreement with previous tools but use far fewer genes. We determined that several AIPS-defined pathways are prognostic across and within molecularly and clinically define subtypes (two-sided log-rank test false discovery rate (FDR) <5%). Interestingly, 74.5% (1291/1733) of the models are able to distinguish patients with luminal A cancer from those with luminal B cancer (Fisher's exact test FDR <5%). CONCLUSION: AIPS represents the first tool that would allow an individual breast cancer patient to obtain a thorough knowledge of the molecular processes active in their tumor from only one individual gene expression (N-of-1) profile.