Adel Tabchy

Evaluation of a 30-Gene Paclitaxel, Fluorouracil, Doxorubicin, and Cyclophosphamide Chemotherapy Response Predictor in a Multicenter Randomized Trial in Breast Cancer

Purpose: We examined in a prospective, randomized, international clinical trial the performance of a previously defined 30-gene predictor (DLDA-30) of pathologic complete response (pCR) to preoperative weekly paclitaxel and fluorouracil, doxorubicin, and cyclophosphamide (T/FAC) chemotherapy, and assessed if DLDA-30 also predicts increased sensitivity to FAC-only chemotherapy. We compared the pCR rates after T/FAC versus FACx6 preoperative chemotherapy. We also did an exploratory analysis to identify novel candidate genes that differentially predict response in the two treatment arms. Experimental Design: Two hundred and seventy-three patients were randomly assigned to receive either weekly paclitaxel × 12 followed by FAC × 4 (T/FAC, n = 138), or FAC × 6 (n = 135) neoadjuvant chemotherapy. All patients underwent a pretreatment fine-needle aspiration biopsy of the tumor for gene expression profiling and treatment response prediction. Results: The pCR rates were 19% and 9% in the T/FAC and FAC arms, respectively (P < 0.05). In the T/FAC arm, the positive predictive value (PPV) of the genomic predictor was 38% [95% confidence interval (95% CI), 21-56%], the negative predictive value was 88% (95% CI, 77-95%), and the area under the receiver operating characteristic curve (AUC) was 0.711. In the FAC arm, the PPV was 9% (95% CI, 1-29%) and the AUC was 0.584. This suggests that the genomic predictor may have regimen specificity. Its performance was similar to a clinical variable–based predictor nomogram. Conclusions: Gene expression profiling for prospective response prediction was feasible in this international trial. The 30-gene predictor can identify patients with greater than average sensitivity to T/FAC chemotherapy. However, it captured molecular equivalents of clinical phenotype. Next-generation predictive markers will need to be developed separately for different molecular subsets of breast cancers. Clin Cancer Res; 16(21); 5351–61. ©2010 AACR.

Location, location, location: a crystal-clear view of autotaxin saturating LPA receptors.

The interaction of autotaxin with its substrates leads to the production of lysophosphatidic acids (LPA), bioactive lipids with an emerging prominent role in inflammation and cancer. Two papers in this issue tell the previously unknown story of autotaxin, from substrate discrimination to highly efficient local delivery of LPA to target receptors.

Proteomic Classification of Breast Cancer

Being a significant health problem that affects patients in various age groups, breast cancer has been extensively studied to date. Recently, molecular breast cancer classification has advanced significantly with the availability of genomic profiling technologies. Proteomic technologies have also advanced from traditional protein assays including enzyme-linked immunosorbent assay, immunoblotting and immunohistochemistry to more comprehensive approaches including mass spectrometry and reverse phase protein lysate arrays (RPPA). The purpose of this manuscript is to review the current protein markers that influence breast cancer prediction and prognosis and to focus on novel advances in proteomic classification of breast cancer.

Quantitative proteomic analysis in breast cancer

Much progress has recently been made in the genomic and transcriptional characterization of tumors. However, historically the characterization of cells at the protein level has suffered limitations in reproducibility, scalability and robustness. Recent technological advances have made it possible to accurately and reproducibly portray the global levels and active states of cellular proteins. Protein microarrays examine the native post-translational conformations of proteins including activated phosphorylated states, in a comprehensive high-throughput mode, and can map activated pathways and networks of proteins inside the cells. The reverse-phase protein microarray (RPPA) offers a unique opportunity to study signal transduction networks in small biological samples such as human biopsy material and can provide critical information for therapeutic decision-making and the monitoring of patients for targeted molecular medicine. By providing the key missing link to the story generated from genomic and gene expression characterization efforts, functional proteomics offer the promise of a comprehensive understanding of cancer. Several initial successes in breast cancer are showing that such information is clinically relevant.

Huntington's disease: A transcriptional report card from the peripheral blood: can it measure disease progression in Huntington's disease?

Huntingtons Disease: A transcriptional report card from the peripheral blood: Can it measure disease progression in Huntingtons disease?

Systematic Identification of Combinatorial Drivers and Targets in Cancer Cell Lines

There is an urgent need to elicit and validate highly efficacious targets for combinatorial intervention from large scale ongoing molecular characterization efforts of tumors. We established an in silico bioinformatic platform in concert with a high throughput screening platform evaluating 37 novel targeted agents in 669 extensively characterized cancer cell lines reflecting the genomic and tissue-type diversity of human cancers, to systematically identify combinatorial biomarkers of response and co-actionable targets in cancer. Genomic biomarkers discovered in a 141 cell line training set were validated in an independent 359 cell line test set. We identified co-occurring and mutually exclusive genomic events that represent potential drivers and combinatorial targets in cancer. We demonstrate multiple cooperating genomic events that predict sensitivity to drug intervention independent of tumor lineage. The coupling of scalable in silico and biologic high throughput cancer cell line platforms for the identification of co-events in cancer delivers rational combinatorial targets for synthetic lethal approaches with a high potential to pre-empt the emergence of resistance.

Evaluation of the Predictive Performance and Regimen Specificity of a 30-Gene Predictor of Pathologic Complete Response in a Prospective Randomized Neoadjuvant Clinical Trial for Stage I-III Breast Cancer.

Purpose: To prospectively evaluate in a randomized trial if a previously reported multigene predictor of pathologic complete response (pCR) to preoperative weekly paclitaxel and fluorouracil-doxorubicin-cyclophosphamide (T/FAC) chemotherapy can accurately predict pCR to neoadjuvant T/FAC chemotherapy, and if it also predicts pCR to FAC only chemotherapy. Furthermore, it is unknown if the T/FAC regimen is superior to 6 courses of FAC; therefore we compare the pCR rates for patients who receive T/FAC versus FACx6 preoperative chemotherapy. Materials and Methods: Patients with stage I-III breast cancer (n=273) were randomly assigned to receive either 12 courses of weekly paclitaxel followed by 4 courses of FAC (T/FAC, n=138), or 6 courses of FAC (FACx6, n=135) neoadjuvant chemotherapy. All patients underwent a pretreatment FNA biopsy of the tumor for gene expression profiling on oligonucleotide microarrays, and treatment response prediction (pCR versus residual disease, RD) was performed using the multigene predictor. Predicted and observed pathologic responses were compared independently in the two treatment arms. Results: The pCR rate was 19% with T/FAC and 9% with FACx6 (p Discussion: Pathologic complete response rate was significantly higher in the T/FAC arm compared to the FACx6 arm indicating a higher efficacy of the paclitaxel containing arm. Patients who were predicted to achieve pCR to T/FAC had a significantly higher pCR rate (38%) than unselected patients (19%) or patients predicted to have RD (12%) when treated with this regimen. These results confirm that the multigene predictor can identify patients with greater than average sensitivity to T/FAC chemotherapy. Citation Information: Cancer Res 2009;69(24 Suppl):Abstract nr 101.

Correction: Systematic Identification of Combinatorial Drivers and Targets in Cancer Cell Lines.

There were errors in Tables 1 and 2. The correct versions of the Tables are available here: Table 1: Table 2:

Abstract 2218: Systematic identification of combinatorial markers of drug sensitivity in cancer cell lines.

Proceedings: AACR 104th Annual Meeting 2013; Apr 6-10, 2013; Washington, DC Purpose: Translating the cancer genome into highly efficacious targets to guide rational therapeutic combinations is a major emerging challenge. Methods: We established an in silico bioinformatic platform in parallel with a high throughput screening platform evaluating the pharmacological activity of 37 novel targeted agents across 669 highly characterized cell lines representing the genetic and tumor-type heterogeneity of human cancers. Analysis of large scale pharmacological data coupled to massive sequencing data on cell lines was performed to systematically identify combinatorial biomarkers of sensitivity and resistance to cancer therapeutics. Genomic predictors discovered in a 141 cell line training set were validated in an independent non-overlapping test set of 359 cell lines screened on 14 of the compounds. Results: We demonstrate combinations of genomic events that are co-occurring or mutually exclusive and act as co-drivers in various tumors, representing potential targets for combinatorial intervention in cancer. We find that multiple cooperating genomic events predict response to drug intervention independent of tumor lineage. Conclusions: The coupling of scalable in silico and functional high throughput cancer cell line platforms for the identification of co-events in cancer delivers rational combinatorial targets for synthetic lethal approaches with a high potential to prevent the emergence of resistance. Citation Format: Adel Tabchy, Nevine Eltonsy, Gordon B. Mills. Systematic identification of combinatorial markers of drug sensitivity in cancer cell lines. [abstract]. In: Proceedings of the 104th Annual Meeting of the American Association for Cancer Research; 2013 Apr 6-10; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2013;73(8 Suppl):Abstract nr 2218. doi:10.1158/1538-7445.AM2013-2218

Abstract 4554: QPCR and sequence analysis of DNA template from a microfluidic CTC isolation platform

Proceedings: AACR 103rd Annual Meeting 2012‐‐ Mar 31‐Apr 4, 2012; Chicago, IL Sequence analysis and quantitative allele specific PCR (QPCR) methods permit genetic profiling of cancer for targeted therapeutic selection; such personalized treatments have been associated with improved outcomes in cancer. Circulating tumor cells (CTC) offer a minimally-invasive opportunity for serial patient sampling, and potentially a means of tracking the molecular evolution that underlies the behavior and response phenotype of the disease including potential therapeutic response markers. Acquiring these types of patient profiles requires a platform and workflow providing reliable detection and recovery of small numbers of mutation-bearing CTC from a blood sample. Availability of such an enabling platform is a necessary prerequisite to the clinical correlation studies needed to demonstrate the utility of mutation-bearing CTC to patient care. We have successfully purified CTCs and converted them into DNA template of sufficient purity and quality to support multiple non-overlapping advanced molecular characterizations. Beginning with whole human blood spiked with defined numbers of cultured cancer cells as surrogates for CTCs, cells were successfully fluid-phase labeled using an anti-EpCAM antibody ferrofluid. Using a proprietary microfluidic sheath flow technology, EpCAM positive, cytokeratin staining cells were selected from 2 to 4 ml of labeled blood. This method produced sufficient DNA template for multiple analyses per patient sample. QPCR analysis of these templates demonstrated reproducible detection of fewer than 1% target cells in a background of non-target cells, allowing detection of the KRAS G12S mutation from as few as 5 recaptured cancer cells. The same DNA templates were then used for hybrid capture and next-generation sequencing of a panel of more than 200 cancer-related genes. This sequencing platform was able to detect multiple somatic mutations in genomic DNA templates produced from samples containing as few as 10 cancer cells per milliliter of blood. Together these data provide initial proof-of-concept for a system capable of detecting and characterizing mutations across any specified set of genes within purified CTC populations. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 103rd Annual Meeting of the American Association for Cancer Research; 2012 Mar 31-Apr 4; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2012;72(8 Suppl):Abstract nr 4554. doi:1538-7445.AM2012-4554