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Tumors with DNA damage repair (DDR) deficiency accumulate genomic alterations that may serve as neoantigens and increase sensitivity to immune checkpoint inhibitor. However, over half of DDR-deficient tumors are refractory to immunotherapy, and it remains unclear which mutations may promote immunogenicity in which cancer types. We integrate deleterious somatic and germline mutations and methylation data of DDR genes in 10,080 cancers representing 32 cancer types and evaluate the associations of these alterations with tumor neoantigens and immune infiltrates. Our analyses identify DDR pathway mutations that are associated with higher neoantigen loads, adaptive immune markers, and survival outcomes of immune checkpoint inhibitor-treated animal models and patients. Different immune phenotypes are associated with distinct types of DDR deficiency, depending on the cancer type context. The comprehensive catalog of immune response-associated DDR deficiency may explain variations in immunotherapy outcomes across DDR-deficient cancers and facilitate the development of genomic biomarkers for immunotherapy., Competing Interests: L.P. has received consulting fees and honoraria from Pfizer, AstraZeneca, Merck, Novartis, Bristol-Myers Squibb, Genentech, Eisai, Pieris, Immunomedics, Seattle Genetics, Clovis, Syndax, H3Bio, and Daiichi. A.L. has received grant support from Pfizer and Genentech for unrelated projects. The rest of the authors declare no competing interests., (© 2021 The Author(s).)

Assessment of programmed cell death-ligand 1 (PD-L1) expression by immunohistochemistry (IHC) is the definite diagnostic test to guide treatment for patients with advanced-stage non-small cell lung cancer. Intratumoral heterogeneity and discrepancy of PD-L1 expression between primary and metastatic lesions may increase the risk of tumor misclassification. We performed a retrospective study of the Foundation Medicine, Inc clinical database on lung cancer cases that were evaluated for PD-L1 expression by IHC in the context of routine care. All cases were assessed with the Food and Drug Administration-approved 22C3 pharmDx assay and scoring system. 15,028 lung cancer cases, including 8285 primary tumors and 6743 unmatched metastatic lesions were analyzed. Metastatic lesions (mets) were more frequently high positive (tumor proportion score (TPS) ≥50%) for PD-L1 expression than primary lesions (33.8% vs 28.4%; OR, 1.28; 95% CI, 1.19 to 1.37; p<0.001). Higher levels in mets than primaries were seen in samples from lymph nodes, pleural fluid, soft tissue and adrenal gland but not in those from liver, brain and bone. Metastatic lesions of patients with non-squamous histology were more likely to have TPS ≥50% in comparison with primary (OR, 1.37; 95% CI, 1.27 to 1.49; p<0.001), but this was not the case for patients with squamous histology (OR, 0.89; 95% CI, 0.74 to 1.06; p=0.197). PD-L1 expression varies with respect to histologic subtype, sampling site and gender, but is generally higher in metastatic sites. This observation may affect future patient management and trial design., Competing Interests: Competing interests: RH, JH, BA, SR and JSR are employees of Foundation Medicine. DLR has served as an advisor for AstraZeneca, Agendia, Amgen, BMS, Cell Signaling Technology, Cepheid, Daiichi Sankyo, Genoptix/Novartis, GSK, Konica Minolta, Merck, NanoString, PAIGE.AI, Perkin Elmer, Roche, Ventana and Ultivue, and received research funding from AstraZeneca, Cepheid, NavigateBP, NextCure, Nanostring, Lilly and Ultivue. LP has received consulting fees and honoraria from AstraZeneca, Merck, Novartis, Bristol-Myers Squibb Genentech, Eisai, Pieris, Immunomedics, Seattle Genetics, Clovis, Syndax, H3Bio and Daiichi., (© Author(s) (or their employer(s)) 2021. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

Background: Our objective was to assess whether modifications to a customized targeted RNA sequencing (RNAseq) assay to include unique molecular identifiers (UMIs) that collapse read counts to their source mRNA counts would improve quantification of transcripts from formalin-fixed paraffin-embedded (FFPE) tumor tissue samples. The assay (SET4) includes signatures that measure hormone receptor and PI3-kinase related transcriptional activity (SET ER/PR and PI3Kges), and measures expression of selected activating point mutations and key breast cancer genes., Methods: Modifications included steps to introduce eight nucleotides-long UMIs during reverse transcription (RT) in bulk solution, followed by polymerase chain reaction (PCR) of labeled cDNA in droplets, with optimization of the polymerase enzyme and reaction conditions. We used Lin's concordance correlation coefficient (CCC) to measure concordance, including precision (Rho) and accuracy (Bias), and nonparametric tests (Wilcoxon, Levene's) to compare the modified (NEW) SET4 assay to the original (OLD) SET4 assay and to whole transcriptome RNAseq using RNA from matched fresh frozen (FF) and FFPE samples from 12 primary breast cancers., Results: The modified (NEW) SET4 assay measured single transcripts (p< 0.001) and SET ER/PR (p=0.002) more reproducibly in technical replicates from FFPE samples. The modified SET4 assay was more precise for measuring single transcripts (Rho 0.966 vs 0.888, p< 0.01) but not multigene expression signatures SET ER/PR (Rho 0.985 vs 0.968) or PI3Kges (Rho 0.985 vs 0.946) in FFPE, compared to FF samples. It was also more precise than wtRNAseq of FFPE for measuring transcripts (Rho 0.986 vs 0.934, p< 0.001) and SET ER/PR (Rho 0.993 vs 0.915, p=0.004), but not PI3Kges (Rho 0.988 vs 0.945, p=0.051). Accuracy (Bias) was comparable between protocols. Two samples carried a PIK3CA mutation, and measurements of transcribed mutant allele fraction was similar in FF and FFPE samples and appeared more precise with the modified SET4 assay. Amplification efficiency (reads per UMI) was consistent in FF and FFPE samples, and close to the theoretically expected value, when the library size exceeded 400,000 aligned reads., Conclusions: Modifications to the targeted RNAseq protocol for SET4 assay significantly increased the precision of UMI-based and reads-based measurements of individual transcripts, multi-gene signatures, and mutant transcript fraction, particularly with FFPE samples.

The US Food and Drug Administration (FDA) approved the PD-L1 immunohistochemical assay, SP142, as a companion test to determine eligibility for atezolizumab therapy in patients with advanced triple negative breast cancer (TNBC) but data in lung cancer studies suggest the assay suffers from poor reproducibility. We sought to evaluate reproducibility and concordance in PD-L1 scoring across multiple pathologists. Full TNBC sections were stained with SP142 and SP263 assays and interpreted for percentage (%) immune cell (IC) staining by 19 pathologists from 14 academic institutions. Proportion of PD-L1 positive cases (defined as ≥1% IC) was determined for each assay as well as concordance across observers. We utilized a new method we call Observers Needed to Evaluate Subjective Tests (ONEST) to determine the minimum number of evaluators needed to estimate concordance between large numbers of readers, as occurs in the real-world setting. PD-L1 was interpreted as positive with the SP142 assay in an average 58% of cases compared with 78% with SP263 (p < 0.0001). IC positive continuous scores ranged from 1 to 95% (mean = 20%) and 1 to 90% (mean = 10%) for SP263 and SP142, respectively. With SP142, 26 cases (38%) showed complete two category (<1% vs. ≥1%) concordance; with SP263, 38 cases (50%) showed complete agreement. The intraclass correlation coefficient (ICC) for two category scoring of SP263 and SP142 was 0.513 and 0.560. ONEST plots showed decreasing overall percent agreement (OPA) as observer number increased, reaching a low plateau of 0.46 at ten observers for SP263 and 0.41 at eight observers for SP142. IC scoring with both assays showed poor reproducibility across multiple pathologists with ONEST analysis suggesting more than half of pathologists will disagree about IC scores. This could lead to many patients either receiving atezolizumab when they are unlikely to benefit, or not receiving atezolizumab when they may benefit.

Purpose: We performed whole-exome sequencing (WES) of pre- and posttreatment cancer tissues to assess the somatic mutation landscape of tumors before and after neoadjuvant taxane and anthracycline chemotherapy with or without bevacizumab., Experimental Design: Twenty-nine pretreatment biopsies from the SWOG S0800 trial were subjected to WES to identify mutational patterns associated with response to neoadjuvant chemotherapy. Nine matching samples with residual cancer after therapy were also analyzed to assess changes in mutational patterns in response to therapy., Results: In pretreatment samples, a higher proportion of mutation signature 3, a BRCA-mediated DNA repair deficiency mutational signature, was associated with higher rate of pathologic complete response (pCR; median signature weight 24%, range 0%-38% in pCR vs. median weight 0%, range 0%-19% in residual disease, Wilcoxon rank sum, Bonferroni P = 0.007). We found no biological pathway level mutations associated with pCR or enriched in posttreatment samples. We observed statistically significant enrichment of high functional impact mutations in the "E2F targets" and "G 2 -M checkpoint" pathways in residual cancer samples implicating these pathways in resistance to therapy and a significant depletion of mutations in the "myogenesis pathway" suggesting the cells harboring these variants were effectively eradicated by therapy., Conclusions: These results suggest that genomic disturbances in BRCA-related DNA repair mechanisms, reflected by a dominant mutational signature 3, confer increased chemotherapy sensitivity. Cancers that survive neoadjuvant chemotherapy frequently have alterations in cell-cycle-regulating genes but different genes of the same pathways are affected in different patients., (©2020 American Association for Cancer Research.)

Purpose: To independently validate two biomarkers, a 44-gene DNA damage immune response (DDIR) signature and stromal tumor-infiltrating lymphocytes (sTILs), as prognostic markers in patients with triple-negative breast cancer (TNBC) treated with adjuvant doxorubicin (A) and cyclophosphamide (C) in SWOG 9313., Methods: Four hundred twenty-five centrally determined patient cases with TNBC from S9313 were identified. DDIR signature was performed on RNA isolated from formalin-fixed paraffin-embedded tumor tissue, and samples were classified as DDIR negative or positive using predefined cutoffs. Evaluation of sTILs was performed as described previously. Markers were tested for prognostic value for disease-free survival (DFS) and overall survival (OS) using Cox regression models adjusted for treatment assignment, nodal status, and tumor size., Results: Among 425 patients with TNBC, 33% were node positive. DDIR was tested successfully in 90% of patients (381 of 425), 62% of which were DDIR signature positive. DDIR signature positivity was associated with improved DFS (hazard ratio [HR], 0.67; 95% CI, 0.48 to 0.92; P = .015) and OS (HR, 0.61; 95% CI, 0.43 to 0.89; P = .010). sTILs density assessment was available in 99% of patients and was associated with improved DFS (HR, 0.70; 95% CI, 0.51 to 0.96; P = .026 for sTILs density ≥ 20% v < 20%) and OS (HR, 0.59; 95% CI, 0.41 to 0.85; P = .004 for sTILs density ≥ 20% v < 20%). DDIR signature score and sTILs density were moderately correlated ( r = 0.60), which precluded statistical significance for DFS in a joint model. Three-year DFS and OS in a subgroup of patients with DDIR positivity and T1c/T2N0 disease were 88% and 94%, respectively., Conclusion: The prognostic role of sTILs and DDIR in early-stage TNBC was confirmed. DDIR signature conferred improved prognosis in two thirds of patients with TNBC treated with adjuvant AC. DDIR signature has the potential to stratify outcome and to identify patients with less projected benefit after AC chemotherapy.

Background: The role of tumor-associated macrophages (TAMs) in the cancer immune landscape and their potential as treatment targets or modulators of response to treatment are gaining increasing interest. TAMs display high molecular and functional complexity. Therefore their objective assessment as breast cancer biomarkers is critical. The aims of this study were to objectively determine the in situ expression and significance of TAM biomarkers (CD68, CD163, and MMP-9) in breast cancer and to identify subclasses of patients who could benefit from TAM-targeting therapies., Methods: We measured CD68, CD163, and MMP-9 protein expression in formalin-fixed paraffin-embedded tissues of breast carcinomas represented in tissue microarray format using multiplexed quantitative immunofluorescence (QIF) in two independent Yale cohorts: cohort A-n = 398, estrogen receptor-positive (ER + ) and ER - cases-and the triple-negative breast cancer (TNBC)-only cohort B (n = 160). Associations between macrophage markers, ER status, and survival were assessed. Protein expression measured by QIF was compared with mRNA expression data from the METABRIC study., Results: All three macrophage markers were co-expressed, displaying higher expression in ER - cancers. High pan-macrophage marker CD68 correlated with poorer overall survival (OS) only in ER - cases of cohort A (P = 0.02). High expression of CD163 protein in TAMs was associated with improved OS in ER - cases (cohort A, P = 0.03 and TNBC cohort B, P = 0.04, respectively) but not in ER + cancers. MMP-9 protein was not individually associated with OS. High expression of MMP-9 in the CD68 + /CD163 + TAMs was associated with worse OS in ER + tumors (P <0.001) but not in ER - cancers. In the METABRIC dataset, mRNA levels followed the co-expression pattern observed in QIF but did not always show the same trend regarding OS., Conclusions: Macrophage activity markers correlate with survival differently in ER + and ER - cancers. The association between high co-expression and co-localization of MMP-9/CD163/CD68 and poor survival in ER + cancers suggests that these cancers may be candidates for macrophage-targeted therapies.

Purpose: Currently, there are only a few software tools designed to assist physicians to translate molecular abnormalities in the cancer genome into potential treatment options. There is a pressing need to develop software to reliably identify known targeted therapies and experimental treatments for patients on the basis of the results of tumor DNA sequencing., Methods: The TQuest platform includes a data layer, data acquisition layer, search engine, and user interface. It identifies associations between one or more molecular targets and therapeutic options. The data layer consists of indexed interventional clinical trials and an expert-curated database of clinically or experimentally validated associations between mutations and drug response. The data acquisition layer includes an information-harvesting module that keeps an up-to-date full-text index of clinical trials by crawling clinicaltrials.gov and combining it with US Food and Drug Administration label data. The user interface is a Web-based module that allows users to upload genomic variants, tumor morphology, and diagnosis. The search results are qualified and ranked by a relevance score., Results: We have manually curated information for 368 distinct genomic variants of 162 gene targets corresponding to 863 drug and target interactions. The platform currently contains a full-text index of approximately 80,000 interventional clinical trials. We applied TQuest to molecular data from 73 metastatic breast cancers. TQuest identified a total of 276 drugs as potential therapeutic options, ranging from one to 103 per patient., Conclusion: TQuest correctly identified all US Food and Drug Administration-approved drugs and routine indications for all cases and also identified many additional drugs that were used in the context of a given molecular abnormality in various clinical trials. The prototype Web application is available at www.tquest.us , and the source code is open and available on GitHub.

Background: Sequence variations in coding and non-coding regions of the genome can affect gene expression and signalling pathways, which in turn may influence disease outcome., Methods: In this study, we integrated somatic mutations, gene expression and clinical data from 930 breast cancer patients included in the TCGA database. Genes associated with single mutations in molecular breast cancer subtypes were identified by the Mann-Whitney U-test and their prognostic value was evaluated by Kaplan-Meier and Cox regression analyses. Results were confirmed using gene expression profiles from the Metabric data set (n = 1988) and whole-genome sequencing data from the TCGA cohort (n = 117)., Results: The overall mutation rate in coding and non-coding regions were significantly higher in ER-negative/HER2-negative tumours (P = 2.8E-03 and P = 2.4E-07, respectively). Recurrent sequence variations were identified in non-coding regulatory regions of several cancer-associated genes, including NBPF1, PIK3CA and TP53. After multivariate regression analysis, gene signatures associated with three coding mutations (CDH1, MAP3K1 and TP53) and two non-coding variants (CRTC3 and STAG2) in cancer-related genes predicted prognosis in ER-positive/HER2-negative tumours., Conclusions: These findings demonstrate that sequence alterations influence gene expression and oncogenic pathways, possibly affecting the outcome of breast cancer patients. Our data provide potential opportunities to identify non-coding variations with functional and clinical relevance in breast cancer.

Importance: Why some triple-negative breast cancers (TNBCs) have high and others have low immune cell infiltration is unknown. Understanding how immune surveillance shapes the cancer genome could help in the selection of patients and the development of more effective immunotherapy strategies., Objective: To examine the association between genomic metrics and the extent of immune infiltration in TNBCs., Design, Setting, and Participants: This study, performed from June 1, 2015, through January 31, 2017, used DNA and RNA sequencing data and messenger RNA expression results from The Cancer Genome Atlas (TCGA) breast cancer data set (n = 1215) to calculate previously described immune metagene expression values and histologic lymphocyte counts to quantify immune infiltration and assign prognostic categories to TNBCs. It used the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) data set as an independent validation cohort. The study compared clonal heterogeneity, somatic total mutational load, neoantigen load, and somatic copy number alteration levels between immune-rich TNBC cohorts with good prognosis and immune-poor TNBC cohorts with poor prognosis. The study also compared the distribution of mutations in 119 canonical cancer genes., Main Outcomes and Measures: Correlation between immune prognostic category and genomic metrics of the cancer., Results: This study of 193 TNBC samples with patient survival information found an inverse association between clonal heterogeneity and immune metagene expression (ρ = −0.395, P = 2 × 10−8). The study also found an inverse association between immune metagene expression and somatic copy number alteration levels (ρ = −0.484, P = 2 × 10−10). Lymphocyte-rich TNBCs with good prognosis had significantly lower mutation and neoantigen counts than did lymphocyte-poor TNBCs with poor prognosis. The robustness of the study results was confirmed by using various immune metagenes in the same TCGA data set and in the independent METABRIC data set., Conclusions and Relevance: This study suggests that immune-rich TNBCs may be under an immune surveillance that continuously eliminates many immunogenic clones, resulting in lower clonal heterogeneity. These cancers may also represent the subset of TNBCs that could derive benefit from immune checkpoint inhibitor therapy to tilt the balance in favor of the immune system.

MicroRNAs (miRNAs) simultaneously modulate different oncogenic networks, establishing a dynamic system of gene expression and pathway regulation. In this study, we analyzed global miRNA and messenger RNA (mRNA) expression profiles of 17 cell lines representing different molecular breast cancer subtypes. Spearman's rank correlation test was used to evaluate the correlation between miRNA and mRNA expression. Hierarchical clustering and pathway analysis were also performed. Publicly available gene expression profiles ( n = 699) and tumor tissues ( n = 80) were analyzed to assess the relevance of key miRNA-regulated pathways in human breast cancer. We identified 39 significantly deregulated miRNAs, and the integration between miRNA and mRNA data revealed the importance of immune-related pathways, particularly the Oncostatin M (OSM) signaling, associated with mesenchymal-like breast cancer cells. OSM levels correlated with genes involved in the inflammatory response, epithelial-to-mesenchymal transition (EMT), and epidermal growth factor (EGF) signaling in human estrogen receptor (ER)-negative/human epidermal growth factor receptor 2 (HER2)-negative breast cancer. Our results suggest that the deregulation of specific miRNAs may cooperatively impair immune and EMT pathways. The identification of the OSM inflammatory pathway as an important mediator of EMT in triple-negative breast cancer (TNBC) may provide a novel potential opportunity to improve therapeutic strategies., Competing Interests: Conflicts of Interests The authors declare no conflict of interest.

Unlabelled: : Advances in DNA and RNA sequencing revealed substantially greater genomic complexity in breast cancer than simple models of a few driver mutations would suggest. Only very few, recurrent mutations or copy-number variations in cancer-causing genes have been identified. The two most common alterations in breast cancer are TP53 (affecting the majority of triple-negative breast cancers) and PIK3CA (affecting almost half of estrogen receptor-positive cancers) mutations, followed by a long tail of individually rare mutations affecting <1%-20% of cases. Each cancer harbors from a few dozen to a few hundred potentially high-functional impact somatic variants, along with a much larger number of potentially high-functional impact germline variants. It is likely that it is the combined effect of all genomic variations that drives the clinical behavior of a given cancer. Furthermore, entirely new classes of oncogenic events are being discovered in the noncoding areas of the genome and in noncoding RNA species driven by errors in RNA editing. In light of this complexity, it is not unexpected that, with the exception of HER2 amplification, no robust molecular predictors of benefit from targeted therapies have been identified. In this review, we summarize the current genomic portrait of breast cancer, focusing on genetic aberrations that are actively being targeted with investigational drugs., Implications for Practice: Next-generation sequencing is now widely available in the clinic, but interpretation of the results is challenging, and its impact on treatment selection is often limited. This work provides an overview of frequently encountered molecular abnormalities in breast cancer and discusses their potential therapeutic implications. This review emphasizes the importance of administering investigational targeted therapies, or off-label use of approved targeted drugs, in the context of a formal clinical trial or registry programs to facilitate learning about the clinical utility of tumor target profiling., Competing Interests: of potential conflicts of interest may be found at the end of this article., (©AlphaMed Press.)

Extensive reprogramming of cellular energy metabolism is a hallmark of cancer. Despite its importance, the molecular mechanism controlling this tumour metabolic shift remains not fully understood. Here we show that 14-3-3σ regulates cancer metabolic reprogramming and protects cells from tumorigenic transformation. 14-3-3σ opposes tumour-promoting metabolic programmes by enhancing c-Myc poly-ubiquitination and subsequent degradation. 14-3-3σ demonstrates the suppressive impact on cancer glycolysis, glutaminolysis, mitochondrial biogenesis and other major metabolic processes of tumours. Importantly, 14-3-3σ expression levels predict overall and recurrence-free survival rates, tumour glucose uptake and metabolic gene expression in breast cancer patients. Thus, these results highlight that 14-3-3σ is an important regulator of tumour metabolism, and loss of 14-3-3σ expression is critical for cancer metabolic reprogramming. We anticipate that pharmacologically elevating the function of 14-3-3σ in tumours could be a promising direction for targeted anticancer metabolism therapy development in future.

Background: Studies have shown that antibodies targeting the intracellular (ICD) or extracellular domains (ECD) of human epidermal growth factor receptor 2 (HER2) are equivalent when traditional methods are used. We describe a new method to quantify ICD and ECD expression separately and assess the prognostic value of domain-specific HER2 results in patients who received adjuvant trastuzumab therapy., Methods: We measured HER2 protein expression with quantitative immunofluorescence (QIF) in tissue microarrays (TMA) using two different antibodies targeting the ICD (CB11 and A0485) and ECD (SP3 and D8F12). We assessed the prognostic value of ICD and ECD expression in 180 patients from a clinical trial of adjuvant chemotherapy followed by trastuzumab (HeCOG 10/05). We performed an exploratory univariate domain-specific, disease-free survival (DFS) analysis and compared DFS functions with Kaplan-Meier estimates. All statistical tests were two-sided., Results: HER2 ICD expression by QIF showed slightly higher sensitivity to predict ERBB2 (HER2) gene amplification than ECD expression, which was more specific and had higher positive predictive value. In the HeCOG 10/05 trial specimens, 15% of cases showed discordant results for ICD and ECD expression. High ECD was statistically associated with longer DFS (log-rank P = .049, HR = 0.31, 95% CI = 0.144 to 0.997), while ICD status was not. Among patients with low ECD, there was no difference in DFS by ICD status. However, when ICD was high, high ECD was statistically associated with longer DFS (log-rank P = .027, HR = 0.23, 95% CI = 0.037 to 0.82) compared with low ECD., Conclusion: Quantitative measurements of HER2 ICD and ECD expression in breast cancer suggest a subclassification of HER2-positive tumors. Trastuzumab-treated patients with high ECD showed better DFS than patients with low ECD. This suggests differential benefit from trastuzumab therapy based on HER2 ECD expression., (© The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.)

Programmed death 1 ligand 1 (PD-L1) is an immune regulatory molecule that limits antitumor immune activity. Targeting of PD-L1 and other immune checkpoint proteins has shown therapeutic activity in various tumor types. The expression of PD-L1 and its correlation with response to neoadjuvant chemotherapy in breast cancer has not been studied extensively. Our goal was to assess PD-L1 expression in a cohort of breast cancer patients treated with neoadjuvant chemotherapy. Pretreatment biopsies from 105 patients with breast cancer from Yale New Haven Hospital that subsequently received neoadjuvant chemotherapy were assessed for PD-L1 protein expression by automated quantitative analysis with a rabbit monoclonal antibody (E1L3N) to the cytoplasmic domain of PD-L1. In addition, tumor-infiltrating lymphocytes (TIL) were assessed on hematoxylin and eosin slides. PD-L1 expression was observed in 30% of patients, and it was positively associated with hormone-receptor-negative and triple-negative status and high levels of TILs. Both TILs and PD-L1 measured in the epithelium or stroma predicted pathologic complete response (pCR) to neoadjuvant chemotherapy in univariate and multivariate analyses. However, because they are strongly associated, TILs and PD-L1 cannot both be included in a significant multivariate model. PD-L1 expression is prevalent in breast cancer, particularly hormone-receptor-negative and triple-negative patients, indicating a subset of patients that may benefit from immune therapy. Furthermore, PD-L1 and TILs correlate with pCR, and high PD-L1 predicts pCR in multivariate analysis., (©2014 American Association for Cancer Research.)

There is growing consensus that multigene prognostic tests provide useful complementary information to tumor size and grade in estrogen receptor (ER)-positive breast cancers. The tests primarily rely on quantification of ER and proliferation-related genes and combine these into multivariate prediction models. Since ER-negative cancers tend to have higher proliferation rates, the prognostic value of current multigene tests in these cancers is limited. First-generation prognostic signatures (Oncotype DX, MammaPrint, Genomic Grade Index) are substantially more accurate to predict recurrence within the first 5 years than in later years. This has become a limitation with the availability of effective extended adjuvant endocrine therapies. Newer tests (Prosigna, EndoPredict, Breast Cancer Index) appear to possess better prognostic value for late recurrences while also remaining predictive of early relapse. Some clinical prediction problems are more difficult to solve than others: there are no clinically useful prognostic signatures for ER-negative cancers, and drug-specific treatment response predictors also remain elusive. Emerging areas of research involve the development of immune gene signatures that carry modest but significant prognostic value independent of proliferation and ER status and represent candidate predictive markers for immune-targeted therapies. Overall metrics of tumor heterogeneity and genome integrity (for example, homologue recombination deficiency score) are emerging as potential new predictive markers for platinum agents. The recent expansion of high-throughput technology platforms including low-cost sequencing of circulating and tumor-derived DNA and RNA and rapid reliable quantification of microRNA offers new opportunities to build extended prediction models across multiplatform data.

In this paper, we will review the data on stromal components and immunological parameters in the cancer microenvironment as prognostic and predictive markers in breast cancer. Host immunological response to cancer has gained importance because of recent breakthroughs in immunotherapy. Currently, molecular and clinical subtyping of breast cancer is solely based on the molecular features of the cancer cells without considering the importance of stromal components. There is now clear evidence that infiltrating immune and inflammatory cells influence the biology and clinical course of breast cancer. However, the prognostic and predictive function of immune cells differs between breast cancer subtypes. Immune parameters are established and validated prognostic and predictive markers in triple negative and for HER2 positive breast cancers and may be ready to be used as stratification parameters in clinical trials and as adjunct variables when making clinical decisions. On the other hand, the prognostic and predictive impact is minimal in low grade, luminal A type breast cancers. The strong association between higher lymphocytic infiltration and better outcome (including greater chemotherapy sensitivity) in TNBC and HER2 positive cancers also raises novel therapeutic options that target immune cells to increase their activity. Immune markers also carry the potential to serve as predictive markers to select patients for immunotherapeutic regimens (e.g. checkpoint inhibitors).

Background: Obesity increases the risk of cancer death among postmenopausal women with estrogen receptor-positive (ER+) breast cancer, but the direct evidence for the mechanisms is lacking. The purpose of this study is to demonstrate direct evidence for the mechanisms mediating this epidemiologic phenomenon., Methods: We analyzed transcriptomic profiles of pretreatment biopsies from a prospective cohort of 137 ER+ breast cancer patients. We generated transgenic (MMTV-TGFα;A (y) /a) and orthotopic/syngeneic (A (y) /a) obese mouse models to investigate the effect of obesity on tumorigenesis and tumor progression and to determine biological mechanisms using whole-genome transcriptome microarrays and protein analyses. We used a coculture system to examine the impact of adipocytes/adipokines on breast cancer cell proliferation. All statistical tests were two-sided., Results: Functional transcriptomic analysis of patients revealed the association of obesity with 59 biological functional changes (P < .05) linked to cancer hallmarks. Gene enrichment analysis revealed enrichment of AKT-target genes (P = .04) and epithelial-mesenchymal transition genes (P = .03) in patients. Our obese mouse models demonstrated activation of the AKT/mTOR pathway in obesity-accelerated mammary tumor growth (3.7- to 7.0-fold; P < .001; n = 6-7 mice per group). Metformin or everolimus can suppress obesity-induced secretion of adipokines and breast tumor formation and growth (0.5-fold, P = .04; 0.3-fold, P < .001, respectively; n = 6-8 mice per group). The coculture model revealed that adipocyte-secreted adipokines (eg, TIMP-1) regulate adipocyte-induced breast cancer cell proliferation and invasion. Metformin suppress adipocyte-induced cell proliferation and adipocyte-secreted adipokines in vitro., Conclusions: Adipokine secretion and AKT/mTOR activation play important roles in obesity-accelerated breast cancer aggressiveness in addition to hyperinsulinemia, estrogen signaling, and inflammation. Metformin and everolimus have potential for therapeutic interventions of ER+ breast cancer patients with obesity., (© The Author 2014. Published by Oxford University Press.)

Bone is the most common site of metastasis of breast cancer, affecting most women with advanced disease. Procollagen type I N-terminal propeptide (P1NP), osteocalcin, CTX, and IL-6 are markers of bone turnover. Our objective was to determine whether serum levels of these proteins have clinical utility as predictors of breast cancer metastasis to bone. Blood was collected before treatment from 164 patients with stage I-III breast cancer from September 2001 to December 2008. Serum levels of P1NP, CTX, IL-6, and OC were measured using an automated immunoassay system. Correlations of the levels of these markers with time to bone metastasis development and with overall survival (OS) rate were assessed using Cox proportional hazards regression analysis and the Kaplan-Meier method. Fifty-five patients with stage I-III disease at the time of blood sample collection subsequently experienced metastasis to bone. A baseline P1NP level of at least 75 ng/mL predicted increased risk of bone metastasis (hazard ratio, 2.7 [95 % confidence interval, 1.2-6.0]; P = 0.031) and a poor OS rate (P = 0.031). Serum P1NP levels at or above 75 ng/mL correlate with a short time to development of bone metastasis and low overall survival in patients with stage I-III breast cancer.

Background: The anti-HER2 monoclonal antibody trastuzumab and the tyrosine kinase inhibitor lapatinib have complementary mechanisms of action and synergistic antitumour activity in models of HER2-overexpressing breast cancer. We argue that the two anti-HER2 agents given together would be better than single-agent therapy., Methods: In this parallel groups, randomised, open-label, phase 3 study undertaken between Jan 5, 2008, and May 27, 2010, women from 23 countries with HER2-positive primary breast cancer with tumours greater than 2 cm in diameter were randomly assigned to oral lapatinib (1500 mg), intravenous trastuzumab (loading dose 4 mg/kg [DOSAGE ERROR CORRECTED], subsequent doses 2 mg/kg), or lapatinib (1000 mg) plus trastuzumab. Treatment allocation was by stratified, permuted blocks randomisation, with four stratification factors. Anti-HER2 therapy alone was given for the first 6 weeks; weekly paclitaxel (80 mg/m(2)) was then added to the regimen for a further 12 weeks, before definitive surgery was undertaken. After surgery, patients received adjuvant chemotherapy followed by the same targeted therapy as in the neoadjuvant phase to 52 weeks. The primary endpoint was the rate of pathological complete response (pCR), analysed by intention to treat. This trial is registered with ClinicalTrials.gov, NCT00553358., Findings: 154 patients received lapatinib, 149 trastuzumab, and 152 the combination. pCR rate was significantly higher in the group given lapatinib and trastuzumab (78 of 152 patients [51·3%; 95% CI 43·1-59·5]) than in the group given trastuzumab alone (44 of 149 patients [29·5%; 22·4-37·5]; difference 21·1%, 9·1-34·2, p=0·0001). We recorded no significant difference in pCR between the lapatinib (38 of 154 patients [24·7%, 18·1-32·3]) and the trastuzumab (difference -4·8%, -17·6 to 8·2, p=0·34) groups. No major cardiac dysfunctions occurred. Frequency of grade 3 diarrhoea was higher with lapatinib (36 patients [23·4%]) and lapatinib plus trastuzumab (32 [21·1%]) than with trastuzumab (three [2·0%]). Similarly, grade 3 liver-enzyme alterations were more frequent with lapatinib (27 [17·5%]) and lapatinib plus trastuzumab (15 [9·9%]) than with trastuzumab (11 [7·4%])., Interpretation: Dual inhibition of HER2 might be a valid approach to treatment of HER2-positive breast cancer in the neoadjuvant setting., Funding: GlaxoSmithKline., (Copyright © 2012 Elsevier Ltd. All rights reserved.)

The aims of this study were to compare the performance of six different genomic prognostic markers to predict long-term survival and chemotherapy response on the same patient cohort and assess if clinicopathological variables carry independent prognostic and predictive values. We examined seven clinical variables and six previously described prognostic signatures on 228 tumors from patients who received homogeneous preoperative chemotherapy and had long-term follow-up information for survival. We used the area under the receiver operator characteristic curve (AUC) to compare predictors and also performed univariate and multivariate analyses including the genomic and clinical variables and plotted Kaplan-Meir survival curves. All genomic prognostic markers had statistically similar AUCs and sensitivity to predict 5-year progression-free survival (PFS, sensitivities ranged from 0.591 to 0.773, and AUCs: 0.599-0.673), overall survival (OS, sensitivities: 0.590-0.769, AUCs: 0.596-0.684) and pathologic complete response (pCR, sensitivities: 0.596-0.851, AUCs: 0.614-0.805). In multivariate analysis, the genomic markers were not independent from one another; however, estrogen receptor (Odds Ratio [OR] 7.63, P < 0.001) and HER2 status (OR: 0.37, P = 0.021) showed significant independent predictive values for pCR. Nodal status remained an independent prognostic, but not predictive, variable (OR for PFS: 2.77, P = 0.021, OR for OS: 3.62, P = 0.01). There was moderate to good agreement between different prediction results in pair-wise comparisons. First-generation prognostic-gene signatures predict both chemotherapy response and long-term survival. When multiple predictors are applied to the same case discordant risk prediction frequently occurs.

Background: Genome scale expression profiling of human tumor samples is likely to yield improved cancer treatment decisions. However, identification of clinically predictive or prognostic classifiers can be challenging when a large number of genes are measured in a small number of tumors., Results: We describe an unsupervised method to extract robust, consistent metagenes from multiple analogous data sets. We applied this method to expression profiles from five "double negative breast cancer" (DNBC) (not expressing ESR1 or HER2) cohorts and derived four metagenes. We assessed these metagenes in four similar but independent cohorts and found strong associations between three of the metagenes and agent-specific response to neoadjuvant therapy. Furthermore, we applied the method to ovarian and early stage lung cancer, two tumor types that lack reliable predictors of outcome, and found that the metagenes yield predictors of survival for both., Conclusions: These results suggest that the use of multiple data sets to derive potential biomarkers can filter out data set-specific noise and can increase the efficiency in identifying clinically accurate biomarkers.

Breast cancer is a heterogeneous disease of different subtypes on the molecular, histopathological, and clinical level. Genomic profiling techniques have led to several prognostic and predictive gene signatures of breast cancer that may further refine outcome prediction, especially in clinically equivocal situations. In particular, the predictive value of today's most important therapeutic targets, ER and HER2, are strongly influenced by the proliferative status of the tumor. Genomic assays are generally performed in a centralized manner, whereas routine pathological evaluation is mostly done on a decentralized basis, making the comparison of these methods difficult. Thus, there remains considerable uncertainty about the use of the new molecular markers in routine clinical decision making and their role in patient selection or stratification for future clinical trials. To address this concern, a group of representatives from breast cancer research groups in the areas of breast pathology, genomic profiling, and clinical trials critically reviewed all available data. Consensus recommendations are made on the practical use of molecular markers in breast cancer management and their incorporation into future clinical trials., (Copyright © 2010 American Cancer Society.)

Tumor biomarker studies may generate insights into the biological characteristics that drive the clinical behavior of a cancer. Publication bias and hidden multiple hypotheses testing distort the assessment of the true value of biomarkers. Publication bias from preferential reporting of 'positive' findings is well recognized. Hidden multihypothesis testing arises from several biomarkers being tested by different teams using the same samples. The more hypotheses (that is, biomarker association with outcome) tested, the greater the risk of false-positive findings. These biases inflate the potential clinical validity and utility of published biomarkers while negative results often remain hidden. Trial registries have been developed where all phase II and phase III trials should be listed regardless of study outcome. However, such steps have not been taken to reduce such bias in tumor biomarker research. We propose that a registry should be created for biomarker studies initially focused on studies that use specimens from randomized trials. Further development could include nonrandomized studies and deposition of raw data similar to existing genomic data repositories. The benefits of a comprehensive biomarker study registry include more balanced evaluation of proposed markers, fewer false positive leads in research, and hopefully more rapid identification of promising candidate biomarkers.

Background: We hypothesized that distinct biological processes might be associated with prognosis and chemotherapy sensitivity in the different types of breast cancers., Methods: We performed gene set analyses with BRB-ArrayTools statistical software including 2331 functionally annotated gene sets (ie, lists of genes that correspond to a particular biological pathway or biochemical function) assembled from Ingenuity Pathway Analysis and Gene Ontology databases corresponding to almost all known biological processes. Gene set analysis was performed on gene expression data from three cohorts of 234, 170, and 175 patients with HER2-normal lymph node-negative breast cancer who received no systemic adjuvant therapy to identify gene sets associated prognosis and three additional cohorts of 198, 85, and 62 patients with HER2-normal stage I-III breast cancer who received preoperative chemotherapy to identify gene sets associated with pathological complete response to therapy. These analyses were performed separately for estrogen receptor (ER)-positive and ER-negative breast cancers. Interaction between gene sets and survival and treatment response by breast cancer subtype was assessed in individual datasets and also in pooled datasets. Statistical significance was estimated with permutation test. All statistical tests were two-sided., Results: For ER-positive cancers, from 370 to 434 gene sets were associated with prognosis (P ≤ .05) and from 209 to 267 gene sets were associated with chemotherapy response in analysis by individual dataset. For ER-positive cancers, 131 gene sets were associated with prognosis and 69 were associated with pathological complete response (P ≤.001) in pooled analysis. Increased expression of cell cycle-related gene sets was associated with poor prognosis, and B-cell immunity-related gene sets were associated with good prognosis. For ER-negative cancers, from 175 to 288 gene sets were associated with prognosis and from 212 to 285 gene sets were associated with chemotherapy response. In pooled analyses of ER-negative cancers, 14 gene sets were associated with prognosis and 23 were associated with response. Gene sets involved in sphingolipid and glycolipid metabolism were associated with better prognosis and those involved in base excision repair, cell aging, and spindle microtubule regulation were associated with chemotherapy response., Conclusion: Different biological processes were associated with prognosis and chemotherapy response in ER-positive and ER-negative breast cancers.

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., (©2010 AACR.)

Purpose: We hypothesize that measurement of gene expression related to estrogen receptor α (ER; gene name ESR1) within a breast cancer sample represents intrinsic tumoral sensitivity to adjuvant endocrine therapy., Methods: A genomic index for sensitivity to endocrine therapy (SET) index was defined from genes coexpressed with ESR1 in 437 microarray profiles from newly diagnosed breast cancer, unrelated to treatment or outcome. The association of SET index and ESR1 levels with distant relapse risk was evaluated from microarrays of ER-positive breast cancer in two cohorts who received 5 years of tamoxifen alone as adjuvant endocrine therapy (n = 225 and 298, respectively), a cohort who received neoadjuvant chemotherapy followed by tamoxifen and/or aromatase inhibition (n = 122), and two cohorts who received no adjuvant systemic therapy (n = 208 and 133, respectively)., Results: The SET index (165 genes) was significantly associated with distant relapse or death risk in both tamoxifen-treated cohorts (hazard ratio [HR] = 0.70, 95% CI, 0.56 to 0.88, P = .002; and HR = 0.76, 95% CI, 0.63 to 0.93, P = .007) and in the chemo-endocrine-treated cohort (HR = 0.19; 95% CI, 0.05 to 0.69, P = .011) independently from pathologic response to chemotherapy, but was not prognostic in two untreated cohorts. No distant relapse or death was observed after tamoxifen alone if node-negative and high SET or after chemo-endocrine therapy if intermediate or high SET., Conclusion: The SET index of ER-related transcription predicted survival benefit from adjuvant endocrine therapy, not inherent prognosis. Prior chemotherapy seemed to enhance the efficacy of adjuvant endocrine therapy related to SET index.

The goal of this study was to develop pharmacogenomic predictors in response to standard chemotherapy drugs in breast cancer cell lines and test their predictive value in patients who received treatment with the same drugs. Nineteen human breast cancer cell lines were tested for sensitivity to paclitaxel (T), 5-fluorouracil (F), doxorubicin (A) and cyclophosphamide (C) in vitro. Baseline gene expression data were obtained for each cell line with Affymetrix U133A gene chips, and multigene predictors of sensitivity were derived for each drug separately. These predictors were applied individually and in combination to human gene expression data generated with the same Affymetrix platform from fine needle aspiration specimens of 133 stage I-III breast cancers. Tumor samples were obtained at baseline, and each patient received 6 months of preoperative TFAC chemotherapy followed by surgery. Cell line-derived prediction results were correlated with the observed pathologic response to chemotherapy. Statistically robust differentially expressed genes between sensitive and resistant cells could only be found for paclitaxel. False discovery rates associated with the informative genes were high for all other drugs. For each drug, the top 100 differentially expressed genes were combined into a drug-specific response predictor. When these cell line-based predictors were applied to patient data, there was no significant correlation between observed response and predicted response either for individual drug predictors or combined predictions. Cell line-derived predictors of response to four commonly used chemotherapy drugs did not predict response accurately in patients.

Patient selection is important for targeted therapies, yet phase I/II trials are often underpowered for developing predictors of drug response. The goal of this research was to define genomic predictors for dasatinib that could be prospectively tested in early-phase clinical trials. Gene expression profiles of dasatinib-sensitive and dasatinib-resistant cell lines (n = 23) were compared to develop a dasatinib-sensitivity index (modified DS index). A Src pathway activity index (revised Src index) was defined using genes induced by the Src transfection of mammary epithelial cells and was optimized to be reproducible across cell lines and human specimens. A dasatinib target index was devised using the weighted sum of 19 kinases that bind to dasatinib with variable affinity. The performance of these prediction models was assessed in independent cell lines with known dasatinib sensitivity. The feasibility of applying these genomic tests to human samples was evaluated on 133 biopsies of primary breast cancers. The modified DS index showed 90% accuracy in independent breast cancer cell lines (n = 12) and the target index, but not the revised Src index signature, also distinguished dasatinib-sensitive and dasatinib-resistant cells (P = 0.0024). The genomic predictors showed acceptable reproducibility in replicate cell line and human gene expression data. When all three predictors were applied to the same 133 patient samples, the predictors identified different patient subsets as potentially sensitive. We defined three conceptually different potential predictors of dasatinib response that were reproducible across cell lines and human data. These candidate markers are being tested in a clinical trial to determine their utility.

Purpose: We assessed Tau protein expression in primary breast cancer specimens of patients included in the National Surgical Breast and Bowel Project (NSABP) -B 28 clinical trial. Expression levels were correlated with disease-free survival (DFS) and overall survival (OS). Interaction between this marker and paclitaxel efficacy was also examined., Methods: Tissue microarrays were available for 1,942 patients (63% of all trial participants) who were randomly assigned to receive either four courses of doxorubicin and cyclophosphamide (AC) or AC followed by four courses of adjuvant paclitaxel chemotherapy. All patients who were hormone receptor positive received adjuvant endocrine therapy. Immunohistochemistry was used to measure Tau expression at M. D. Anderson Cancer Center blinded to clinical outcome. Correlation between Tau and OS and DFS was performed by the NSABP Biostatistical Center., Results: Forty-three percent of patients were Tau positive. Tau positivity correlated with estrogen receptor (ER) -positive status (P < .0001), lower histologic grade (P < .001), and lack of human epidermal growth factor receptor 2 (HER2) expression (P < .0001). In univariate analyses, Tau- and ER-positive status were both associated with better DFS and OS (P < .0001) whereas greater tumor size, high grade, lymph node- and HER2-positive status were each associated with worse DFS and OS (P < .0001). In multivariate analysis, the same variables except HER2 remained significant. There was no significant interaction between Tau expression and benefit from paclitaxel in the total population or among ER-positive or -negative patients., Conclusion: High Tau protein expression is associated with better prognosis in patients treated with adjuvant anthracycline and paclitaxel chemotherapy and endocrine therapy, but there was no significant interaction between Tau expression and paclitaxel benefit.

The human epidermal growth factor receptor (HER-2) oncogene encodes a transmembrane tyrosine kinase receptor that has evolved as a major classifier of invasive breast cancer and target of therapy for the disease. The validation of the general prognostic significance of HER-2 gene amplification and protein overexpression in the absence of anti-HER-2 targeted therapy is discussed in a study of 107 published studies involving 39,730 patients, which produced an overall HER-2-positive rate of 22.2% and a mean relative risk for overall survival (OS) of 2.74. The issue of HER-2 status in primary versus metastatic breast cancer is considered along with a section on the features of metastatic HER-2-positive disease. The major marketed slide-based HER-2 testing approaches, immunohistochemistry, fluorescence in situ hybridization, and chromogenic in situ hybridization, are presented and contrasted in detail against the background of the published American Society of Clinical Oncology-College of American Pathologists guidelines for HER-2 testing. Testing issues, such as the impact of chromosome 17 polysomy and local versus central HER-2 testing, are also discussed. Emerging novel HER-2 testing techniques, including mRNA-based testing by real-time polymerase chain reaction and DNA microarray methods, HER-2 receptor dimerization, phosphorylated HER-2 receptors, and HER-2 status in circulating tumor cells, are also considered. A series of biomarkers potentially associated with resistance to trastuzumab is discussed with emphasis on the phosphatase and tensin homologue deleted on chromosome ten/Akt and insulin-like growth factor receptor pathways. The efficacy results for the more recently approved small molecule HER-1/HER-2 kinase inhibitor lapatinib are also presented along with a more limited review of markers of resistance for this agent. Additional topics in this section include combinations of both anti-HER-2 targeted therapies together as well as with novel agents including bevacizumab, everolimus, and tenespimycin. A series of novel HER-2-targeting agents is also presented, including pertuzumab, ertumaxomab, HER-2 vaccines, and recently discovered tyrosine kinase inhibitors. Biomarkers predictive of HER-2 targeted therapy toxicity are included, and the review concludes with a consideration of HER-2 status in the prediction of response to non-HER-2 targeted treatments including hormonal therapy, anthracyclines, and taxanes.

Breast cancer is a clinically heterogeneous disease that can affect individuals with seemingly identical clinicopathologic parameters differently. This clinical heterogeneity is driven to a large extent by abnormal gene expression within tumors. Investigators now have the ability to identify the gene-expression fingerprint of an individual's tumor. This information may be used to rationally design therapeutic targets in the future, and also to predict the clinical course of an individual's disease, including response to a specific treatment. Genetic profiles of tumors are now being correlated with clinical outcome, and several prognostic and predictive indicators have emerged based on this research. There are at least four commercially available predictive or prognostic tests, and several more are looming on the horizon. The data gathered from these tests augment standard diagnostic and prognostic information obtained from traditional clinical pathological variables. The advent of gene-profiling technologies started to change the conduct of clinical trials. In the not too distant future, prospective tissue collection for molecular analysis may become routine in order to stratify patients for treatment arms and to optimize treatment strategies based on molecular features of the cancer. Coordinated efforts among oncologists, pathologists, surgeons, laboratory scientists, statisticians, and regulators will be essential in the quest to incorporate genetic profiling and molecular hypotheses into clinical trial planning and conduct.

Background: DNA microarray technology has emerged as a major tool for exploring cancer biology and solving clinical issues. Predicting a patient's response to chemotherapy is one such issue; successful prediction would make it possible to give patients the most appropriate chemotherapy regimen. Patient response can be classified as either a pathologic complete response (PCR) or residual disease (NoPCR), and these strongly correlate with patient outcome. Microarrays can be used as multigenic predictors of patient response, but probe selection remains problematic. In this study, each probe set was considered as an elementary predictor of the response and was ranked on its ability to predict a high number of PCR and NoPCR cases in a ratio similar to that seen in the learning set. We defined a valuation function that assigned high values to probe sets according to how different the expression of the genes was and to how closely the relative proportions of PCR and NoPCR predictions to the proportions observed in the learning set was. Multigenic predictors were designed by selecting probe sets highly ranked in their predictions and tested using several validation sets., Results: Our method defined three types of probe sets: 71% were mono-informative probe sets (59% predicted only NoPCR, and 12% predicted only PCR), 25% were bi-informative, and 4% were non-informative. Using a valuation function to rank the probe sets allowed us to select those that correctly predicted the response of a high number of patient cases in the training set and that predicted a PCR/NoPCR ratio for validation sets that was similar to that of the whole learning set. Based on DLDA and the nearest centroid method, bi-informative probes proved more successful predictors than probes selected using a t test., Conclusion: Prediction of the response to breast cancer preoperative chemotherapy was significantly improved by selecting DNA probe sets that were successful in predicting outcomes for the entire learning set, both in terms of accurately predicting a high number of cases and in correctly predicting the ratio of PCR to NoPCR cases.

Introduction: In vitro evidence suggests that PIK3CA (phosphatidylinositol 3-kinase, catalytic, alpha polypeptide) activation may be associated with altered chemotherapy sensitivity in cancer., Methods: Tumor DNA from 140 patients with stage II-III breast cancer undergoing neoadjuvant chemotherapy was sequenced for PIK3CA mutations on exons 1, 9, and 20. Mutation status was correlated with clinical/pathological parameters and chemotherapy response as (a) pathological complete response (pCR) versus residual cancer or (b) quantitative residual cancer burden (RCB) scores, including stratification for estrogen receptor (ER) expression status, type of chemotherapy, and by exons., Results: Twenty-three patients (16.4%) harbored a PIK3CA mutation, with 12, 11, and 0 mutations located in exons 9, 20, and 1, respectively. PIK3CA exon 9 mutations were more frequent among node-negative (52% versus 25%; P = 0.012) than node-positive tumors, particularly among ER-positive tumors. pCR rates and RCB scores were similar among patients with the wild-type and mutant PIK3CA genes, even after stratification by ER status, chemotherapy regimen (anthracycline versus anthracycline plus paclitaxel), or exon., Conclusion: PIK3CA mutations are not associated with altered sensitivity to preoperative anthracycline-based or taxane-based chemotherapies in ER-positive and ER-negative breast tumors. In this study, PIK3CA mutation was associated with a decreased rate of node-positive disease, particularly among ER-positive tumors.

Breast cancer is a clinically heterogeneous disease, and it is generally accepted that the different clinical courses of patients with histologically similar tumors are due to molecular differences among cancers. Therefore, detailed molecular analysis of the cancer could yield prognostic information. Recent advances in molecular analytical techniques have led to rapid expansion of novel diagnostics designed to personalize breast cancer care. Diagnostic companies are also increasingly adopting a clinical trial-based approach to develop their products. This article reviews some of the most important advances in this field in the past few years, including the emergence of several multigene and prognostic predictors, as well as methods allowing enumeration of circulating tumor cells that are currently offered as commercially available diagnostic assays.

Introduction: We performed gene expression analysis to identify molecular predictors of resistance to preoperative concomitant trastuzumab and paclitaxel followed by 5-fluorouracil, epirubicin, and cyclophosphamide (T/FEC)., Methods: Pretreatment fine-needle aspiration specimens from 45 patients with HER-2-overexpressing stage II to IIIA breast cancer were subjected to transcriptional profiling and examined for differential expression of various genes and gene sets. The primary endpoint for tumor response was pathologic complete response (pCR). Correlations between pCR and gene expression were sought., Results: The overall pCR rate was 64%. Age, nuclear grade, tumor size, nodal status, quantitative expression of estrogen and HER-2 receptor mRNA, and HER-2 gene copy number showed no correlation with pCR. Results of gene set enrichment analysis suggested that the lower expression of genes involved with CD40 signaling is associated with a greater risk of residual cancer after the preoperative chemotherapy that includes trastuzumab., Conclusion: CD40 signaling may play a role in determining response to trastuzumab-plus-T/FEC therapy in patients with HER-2-overexpressing breast cancer.

Adjuvant chemotherapy improves survival of patients with stage I-III breast cancer but it is being increasingly recognized that the benefit is not equal for all patients. Molecular characteristics of the cancer affect sensitivity to chemotherapy. In general, estrogen-receptor-negative disease is more sensitive to chemotherapy than estrogren-receptor-positive disease. Large-scale genomic analyses of breast cancer suggest that further molecular subsets may exist within the categories defined by hormone receptor status. It is hoped that the new molecular classification schemes might improve patient selection for therapy. Before any new molecular classification (or predictive test) is adopted for routine clinical use, however, several criteria need to be met. There must be an agreed and reproducible method by which to assign molecular class to a new case. Cancers that belong to different molecular classes must show differences in disease outcome and treatment efficacy that affect management and treatment selection. Also desirable are results from prospective clinical trials that demonstrate improved patient outcome when the new test is used in decision-making, compared with the current standard of care. This Review describes the current limitations and future promises of gene-expression-based molecular classification of breast cancer and how it might impact on selection of adjuvant therapy for individual patients.

The HER-2/neu oncogene, a member of the epidermal growth factor receptor or erb gene family, encodes a transmembrane tyrosine kinase receptor that has been linked to prognosis and response to therapy with the anti-HER-2-humanized monoclonal antibody, trastuzumab (Herceptin, Genentech, South San Francisco, CA) in patients with advanced metastatic breast cancer. HER-2/neu status has also been tested for its ability to predict the response of breast cancer to other therapies including hormonal therapies, topoisomerase inhibitors, and anthracyclines. This review includes an analysis of 80 published studies encompassing more than 25,000 patients designed to consider the relative advantages and disadvantages of the various methods of measuring HER-2/neu in clinical breast cancer specimens. Southern blotting, PCR amplification detection, and fluorescence in situ hybridization assays designed to detect HER-2/neu gene amplification are compared with HER-2/neu protein overexpression assays performed by immunohistochemical techniques applied to frozen and paraffin-embedded tissues and enzyme immunoassays performed on tumor cytosols. The significance of HER-2/neu overexpression in ductal carcinoma in situ and the HER-2/neu status in uncommon female breast conditions and male breast cancer are also considered. The role of HER-2/neu testing for the prediction of response to trastuzumab therapy in breast cancer is reviewed along with the current studies designed to test whether HER-2/neu status can predict the response to standard and newer hormonal therapies, cytotoxic chemotherapy, and radiation. The review will also evaluate the status of serum-based testing for circulating HER-2/neu receptor protein and its ability to predict disease outcome and therapy response.

The first part of this two-part review of established and emerging breast cancer biomarkers and molecular diagnostics considers breast cancer predisposition, screening tests for diagnosis, diagnosis using small specimens and metastatic lesions, micrometastatic disease and breast cancer prognosis assessment. Prognostic factors covered in this review include: cytogenetic markers, DNA ploidy and S phase determination, cell proliferation markers, cell cycle regulators and growth factor measurements including epithelial growth factor receptor, HER-2/neu and a variety of other relevant molecules controlling proliferation, differentiation and angiogenesis. The first section of part two will continue the consideration of breast cancer prognostic factors including oncogenes, tumor suppressor genes, cell adhesion molecules, invasion-associated proteins and proteases, hormone receptor proteins, drug resistance proteins, apoptosis regulators, transcription factors, telomerase, DNA repair and methylation and transcriptional profiling using high-density genomic microarrays. The second section of part two will consider the prediction of therapy response using the techniques of pharmacogenetics and pharmacogenomics.