Prospective Molecular Profiling of Canine Cancers Provides a Clinically Relevant Comparative Model for Evaluating Personalized Medicine (PMed) Trials.

Background Molecularly-guided trials (i.e. PMed) now seek to aid clinical decision-making by matching cancer targets with therapeutic options. Progress has been hampered by the lack of cancer models that account for individual-to-individual heterogeneity within and across cancer types. Naturally occurring cancers in pet animals are heterogeneous and thus provide an opportunity to answer questions about these PMed strategies and optimize translation to human patients. In order to realize this opportunity, it is now necessary to demonstrate the feasibility of conducting molecularly-guided analysis of tumors from dogs with naturally occurring cancer in a clinically relevant setting. Methodology A proof-of-concept study was conducted by the Comparative Oncology Trials Consortium (COTC) to determine if tumor collection, prospective molecular profiling, and PMed report generation within 1 week was feasible in dogs. Thirty-one dogs with cancers of varying histologies were enrolled. Twenty-four of 31 samples (77%) successfully met all predefined QA/QC criteria and were analyzed via Affymetrix gene expression profiling. A subsequent bioinformatics workflow transformed genomic data into a personalized drug report. Average turnaround from biopsy to report generation was 116 hours (4.8 days). Unsupervised clustering of canine tumor expression data clustered by cancer type, but supervised clustering of tumors based on the personalized drug report clustered by drug class rather than cancer type. Conclusions Collection and turnaround of high quality canine tumor samples, centralized pathology, analyte generation, array hybridization, and bioinformatic analyses matching gene expression to therapeutic options is achievable in a practical clinical window (\u3c1 \u3eweek). Clustering data show robust signatures by cancer type but also showed patient-to-patient heterogeneity in drug predictions. This lends further support to the inclusion of a heterogeneous population of dogs with cancer into the preclinical modeling of personalized medicine. Future comparative oncology studies optimizing the delivery of PMed strategies may aid cancer drug development

Susan (Wender) Lowenstein-Kitchell \u2748

Susan (Wender) Lowenstein-Kitchell ‘48 began her studies at Bard right after the Army Specialized Training Program (ASTP) ended on campus. She discusses how Bard became independent from Columbia: Millicent McIntosh, then president of Barnard College (another Columbia affiliate) didn’t want competition from a related co-ed institution. Thus, 1944 welcomed the first full class of women at Bard. During her time at Bard, Susan studied music, dance, and composition, and combined all of these fields to create her senior project, a dance concert. After Bard, she pursued a masters in composition from Columbia, and was also able to take lessons with teachers from Julliard. After raising her family, she returned to the workforce by playing for dance classes and teaching masters students at Smith College. Susan reminisces about how grateful she was to be at a school that allowed much more freedom for women, and remarks on the foundation for life that Bard provided her.https://digitalcommons.bard.edu/oral_hist/1085/thumbnail.jp

Reasons samples failed QA/QC.

<p>Reasons samples failed QA/QC.</p

Bioinformatic analysis defines the platform for PMed report generation.

<p>Gene expression data from each tumor was compared to a reference sample set (canine normal tissue compendium, GSE20113 from Gene Expression Omnibus) to obtain a relative gene expression profile. Each gene probeset was represented by a z-score depicting its expression in the tumor in terms of the number of standard-deviations from the mean expression in the reference set. In the iteration of the PMed tools used in this study, data were analyzed by six distinct predictive methodologies (Drug Target Expression, Drug Response Signatures, Drug Sensitivity Signatures, Network Target Activity, Biomarker-Based-Rules-Sensitive, Biomarker-Based-Rules-Insensitive) to identify (or exclude in the case of biomarker resistant rules) potential agents for consideration. All predictions were based on the conversion of canine genomic data into human homologs (for both patient tumor samples and the reference set of normal tissues) prior to the application of the specific algorithms that rely exclusively on human knowledge and/or empirical drug screens using human cell lines (see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0090028#s4" target="_blank">Methods</a>). While individual patient tumor PMed report generation and distribution was the final step in this process, this specific study did not have therapeutic intent and drug prescription was not performed.</p

Study Cohorts.

<p>Study Cohorts.</p

Clinical turn around time.

<p>*Clinical turnaround time for case 0508 (TCC) was an outlier (completed in 212 business hours).</p><p>The expression data was generated in 91 business hours (3.79 days) but there was a delay in the PMed report being sent to investigators. Overall the turn around for sample analysis fits a clinical window and its inclusion in the analysis did not impact the study conclusions.</p