Wong, Shukmei

Abstract We present chromosome-level, phased diploid genome assemblies of two widely used human fibroblast cell lines: BJ (46,XY) and IMR-90 (46,XX). Using Oxford Nanopore, PacBio HiFi, and Hi-C sequencing data, we generated assemblies spanning 5.9 and 6.0 Gbp with diploid quality values exceeding QV 60. To validate structural integrity, we developed KaryoScope, an alignment-free tool for generating computational karyograms from k-mer feature databases. We identify >50 000 structural variants relative to T2T-CHM13v2.0, the majority of which are heterozygous and cell-line-specific. Combining reference-based and de novo gene annotation, we uncover a previously unreported 1 Mbp homozygous duplication at the 16p11.2 locus in BJ, demonstrating that even karyotypically normal cell lines can harbor clinically relevant submicroscopic rearrangements. We show that mapping publicly available short-read, RNA-seq, and ChIP-seq data to sample-matched diploid assemblies substantially improves read alignment and enables haplotype phasing of 23%–28% of short reads. The BJ and IMR-90 assemblies and associated variant calls are publicly available as a resource for the research community.

This prospective phase II clinical trial (Side Out 2) explored the clinical benefits of treatment selection informed by multi‐omic molecular profiling (MoMP) in refractory metastatic breast cancers (MBCs). Core needle biopsies were collected from 32 patients with MBC at trial enrollment. Patients had received an average of 3.94 previous lines of treatment in the metastatic setting before enrollment in this study. Samples underwent MoMP, including exome sequencing, RNA sequencing (RNA‐Seq), immunohistochemistry, and quantitative protein pathway activation mapping by Reverse Phase Protein Microarray (RPPA). Clinical benefit was assessed using the previously published growth modulation index (GMI) under the hypothesis that MoMP‐selected therapy would warrant further investigation for GMI ≥ 1.3 in ≥ 35% of the patients. Of the 32 patients enrolled, 29 received treatment based on their MoMP and 25 met the follow‐up criteria established by the trial protocol. Molecular information was delivered to the tumor board in a median time frame of 14 days (11–22 days), and targetable alterations for commercially available agents were found in 23/25 patients (92%). Of the 25 patients, 14 (56%) reached GMI ≥ 1.3. A high level of DNA topoisomerase I (TOPO1) led to the selection of irinotecan‐based treatments in 48% (12/25) of the patients. A pooled analysis suggested clinical benefit in patients with high TOPO1 expression receiving irinotecan‐based regimens (GMI ≥ 1.3 in 66.7% of cases). These results confirmed previous observations that MoMP increases the frequency of identifiable actionable alterations (92% of patients). The MoMP proposed allows the identification of biomarkers that are frequently expressed in MBCs and the evaluation of their role as predictors of response to commercially available agents. Lastly, this study confirmed the role of MoMP for informing treatment selection in refractory MBC patients: more than half of the enrolled patients reached a GMI ≥ 1.3 even after multiple lines of previous therapies for metastatic disease.

Abstract Purpose: Little is known about the molecular signatures associated with specific metastatic sites in breast cancer. Using comprehensive multi-omic molecular profiling, we assessed whether alterations or activation of the PI3K–AKT–mTOR pathway is associated with specific sites of breast cancer metastasis. Experimental Design: Next-generation sequencing–based whole-exome sequencing was coupled with reverse-phase protein microarray (RPPA) functional signaling network analysis to explore the PI3K–AKT–mTOR axis in 32 pretreated breast cancer metastases. RPPA-based signaling data were further validated in an independent cohort of 154 metastatic lesions from breast cancer and 101 unmatched primary breast tumors. The proportion of cases with PI3K–AKT–mTOR genomic alterations or signaling network activation were compared between hepatic and nonhepatic lesions. Results: PIK3CA mutation and activation of AKT (S473) and p70S6K (T389) were detected more frequently among liver metastases than nonhepatic lesions (P < 0.01, P = 0.056, and P = 0.053, respectively). However, PIK3CA mutations alone were insufficient in predicting protein activation (P = 0.32 and P = 0.19 for activated AKT and p70S6K, respectively). RPPA analysis of an independent cohort of 154 tumors confirmed the relationship between pathway activation and hepatic metastasis [AKT (S473), mTOR (S2448), and 4EBP1 (S65); P < 0.01, P = 0.02, and P = 0.01, respectively]. Similar results were also seen between liver metastases and primary breast tumors [AKT (S473) P < 0.01, mTOR (S2448) P < 0.01, 4EBP1 (S65) P = 0.01]. This signature was lost when primary tumors were compared with all metastatic sites combined. Conclusions: Breast cancer patients with liver metastasis may represent a molecularly homogenized cohort with increased incidence of PIK3CA mutations and activation of the PI3K–AKT–mTOR signaling network. Clin Cancer Res; 23(16); 4919–28. ©2017 AACR.

Abstract Background: While genomic alterations are central players in tumor progression, proteins are the targets for precision therapy. The degree by which “actionable” genomic alterations translate into activated/altered proteins and pathway is still under investigation. Using a multi-OMIC approach from the SideOut 2 metastatic breast cancer (MBC) trial, this study explored the concordance between selected “actionable” genomic alterations and protein expression/activation. Methods: Snap frozen biopsies from 29 MBC patients enrolled in a prospective phase II trial were used for this analysis. Exome WES and RNASeq data was processed using an in-house developed pipeline and identified amplification of CCND1 (6/29), FGFR1 (4/29), and FGF 3, 4, 5, and 19 (4/29) as some of most frequent “actionable” genomic alterations in our MBC cohort. Signaling analysis of the 29 cases was performed using Reverse Phase Protein Microarray coupled with Laser Capture Microdissection. Protein expression/phosphorylation was measured in a continuous scale and classified based on quartile distribution. Concordance between CCND1 amplification and Cyclin D1 expression, along with the activation of FOXM1 T600 and Rb S780, was explored. Amplification of the FGFR1 locus or its ligands was correlated with the level of activation/phosphorylation of FGFR1 Y653/654. Results: While Cyclin D1 protein expression was greater than the population mean for 4/6 (67%) patients with CCND1 amplification, only 2/6 (33%) patients with CCND1 amplification had Cyclin D1 level within the top quartile of the population (n=29). FOXM1 T600 activation was independent from CCND1 amplification, with high levels of FOXM1 T600 predominantly in the CCND1 wild-type population. Only 1/6 (17%) patients with CCND1 amplification had FOXM1 T600 level similar to the top quartile of the population while a second patient was above the population median. Activation of Rb S780 was above the population median, but below the top quartile, in 2/6 (33%) CCND1 amplified patients. Similarly, none of the patients with activation of FGFR Y653/654 equal to the top quartile harbored an FGFR1 amplification. Only 1/4 (25%) patients carrying an FGFR1 amplification had an activation of FGFR Y653/654 above the population median. Similarly, 1/4 (25%) patients with FGF ligand amplification showed FGFR Y653/654 level within the top quartile while three patients had FGFR Y653/654 activation below the population median. No significant results were found between proteomic (below/above the median) and genomic characteristics by Fisher test (p>0.05). Conclusion: Molecular genotyping of “actionable” cancer targets alone may be insufficient in predicting whether the actual drug target protein is expressed and/or activated in any given patient's tumor. Although these results need further validation, the combination of genomic and proteomic data may represent a more informative approach for identifying real molecular drivers of individual lesions as well as “actionable” protein/phosphoprotein targets in the absence of genomic events. Multi-OMIC approaches may lead to more effective stratification in precision medicine trials. Citation Format: Pierobon M, Wong S, Reeded A, Anthony S, Robert N, Northfelt DW, Jahanzeb M, Vocila L, Wulfkuhle J, Dunetz B, Aldrich J, Byron S, Craig D, Liotta L, Carpten J, Petricoin EF. A multi-OMIC analysis to explore the impact of “actionable” genomic alterations on protein pathway activation: Clinical implication for precision medicine in metastatic breast cancer [abstract]. In: Proceedings of the 2016 San Antonio Breast Cancer Symposium; 2016 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2017;77(4 Suppl):Abstract nr P1-07-09.