Supervised Risk Predictor of Breast Cancer Based on Intrinsic Subtypes.

PURPOSE: To improve on current standards for breast cancer prognosis and prediction of chemotherapy benefit by developing a risk model that incorporates the gene expression-based "intrinsic" subtypes luminal A, luminal B, HER2-enriched, and basal-like. METHODS: A 50-gene subtype predictor was developed using microarray and quantitative reverse transcriptase polymerase chain reaction data from 189 prototype samples. Test sets from 761 patients (no systemic therapy) were evaluated for prognosis, and 133 patients were evaluated for prediction of pathologic complete response (pCR) to a taxane and anthracycline regimen. RESULTS: The intrinsic subtypes as discrete entities showed prognostic significance (P = 2.26E-12) and remained significant in multivariable analyses that incorporated standard parameters (estrogen receptor status, histologic grade, tumor size, and node status). A prognostic model for node-negative breast cancer was built using intrinsic subtype and clinical information. The C-index estimate for the combined model (subtype and tumor size) was a significant improvement on either the clinicopathologic model or subtype model alone. The intrinsic subtype model predicted neoadjuvant chemotherapy efficacy with a negative predictive value for pCR of 97%. CONCLUSION: Diagnosis by intrinsic subtype adds significant prognostic and predictive information to standard parameters for patients with breast cancer. The prognostic properties of the continuous risk score will be of value for the management of node-negative breast cancers. The subtypes and risk score can also be used to assess the likelihood of efficacy from neoadjuvant chemotherapy.

The proteomic landscape of soft tissue sarcomas.

Soft tissue sarcomas (STS) are rare and diverse mesenchymal cancers with limited treatment options. Here we undertake comprehensive proteomic profiling of tumour specimens from 321 STS patients representing 11 histological subtypes. Within leiomyosarcomas, we identify three proteomic subtypes with distinct myogenesis and immune features, anatomical site distribution and survival outcomes. Characterisation of undifferentiated pleomorphic sarcomas and dedifferentiated liposarcomas with low infiltrating CD3 + T-lymphocyte levels nominates the complement cascade as a candidate immunotherapeutic target. Comparative analysis of proteomic and transcriptomic profiles highlights the proteomic-specific features for optimal risk stratification in angiosarcomas. Finally, we define functional signatures termed Sarcoma Proteomic Modules which transcend histological subtype classification and show that a vesicle transport protein signature is an independent prognostic factor for distant metastasis. Our study highlights the utility of proteomics for identifying molecular subgroups with implications for risk stratification and therapy selection and provides a rich resource for future sarcoma research.

Relationship between ER expression by IHC or mRNA with Ki67 response to aromatase inhibition: a POETIC study.

BACKGROUND: In clinical practice, oestrogen receptor (ER) analysis is almost entirely by immunohistochemistry (IHC). ASCO/CAP recommends cut-offs of < 1% (negative) and 1-10% (low) cells positive. There is uncertainty whether patients with ER low tumours benefit from endocrine therapy. We aimed to assess IHC and mRNA cut-points for ER versus biological response of primary breast cancer to 2 weeks' aromatase inhibitor treatment as measured by change in Ki67. METHODS: Cases were selected from the aromatase inhibitor treatment group of POETIC. We selected the 15% with the poorest Ki67 response (PR, < 40% Ki67 suppression, n = 230) and a random 30% of the remainder categorised as intermediate (IR, 40-79% Ki67 suppression, n = 150) and good-responders (GR, ≥ 80% Ki67 suppression, n = 230) from HER2 - group. All HER2 + cases available were selected irrespective of their response category (n = 317). ER expression was measured by IHC and qPCR. RESULTS: ER IHC was available from 515 HER2 - and 186 HER2 + tumours and ER qPCR from 367 HER2 - and 171 HER2 + tumours. Ninety-one percentage of patients with ER IHC < 10% were PRs with similar rates in HER2 - and HER2 + cases. At or above ER IHC 10% substantial numbers of patients showed IR or GR. Similar proportions of patients were defined by cut-points of ER IHC < 10% and ER mRNA < 5 units. In addition, loss of PgR expression altered ER anti-proliferation response with 92% of PgR - cases with ER IHC < 40% being PRs. CONCLUSIONS: There was little responsiveness at IHC < 10% and no distinction between < 1% and 1-10% cells positive. Similar separation of PRs from IR/GRs was achieved by IHC and mRNA.

HER2-enriched subtype and novel molecular subgroups drive aromatase inhibitor resistance and an increased risk of relapse in early ER+/HER2+ breast cancer.

BACKGROUND: Oestrogen receptor positive/ human epidermal growth factor receptor positive (ER+/HER2+) breast cancers (BCs) are less responsive to endocrine therapy than ER+/HER2- tumours. Mechanisms underpinning the differential behaviour of ER+HER2+ tumours are poorly characterised. Our aim was to identify biomarkers of response to 2 weeks' presurgical AI treatment in ER+/HER2+ BCs. METHODS: All available ER+/HER2+ BC baseline tumours (n=342) in the POETIC trial were gene expression profiled using BC360™ (NanoString) covering intrinsic subtypes and 46 key biological signatures. Early response to AI was assessed by changes in Ki67 expression and residual Ki67 at 2 weeks (Ki672wk). Time-To-Recurrence (TTR) was estimated using Kaplan-Meier methods and Cox models adjusted for standard clinicopathological variables. New molecular subgroups (MS) were identified using consensus clustering. FINDINGS: HER2-enriched (HER2-E) subtype BCs (44.7% of the total) showed poorer Ki67 response and higher Ki672wk (p<0.0001) than non-HER2-E BCs. High expression of ERBB2 expression, homologous recombination deficiency (HRD) and TP53 mutational score were associated with poor response and immune-related signatures with High Ki672wk. Five new MS that were associated with differential response to AI were identified. HER2-E had significantly poorer TTR compared to Luminal BCs (HR 2.55, 95% CI 1.14-5.69; p=0.0222). The new MS were independent predictors of TTR, adding significant value beyond intrinsic subtypes. INTERPRETATION: Our results show HER2-E as a standardised biomarker associated with poor response to AI and worse outcome in ER+/HER2+. HRD, TP53 mutational score and immune-tumour tolerance are predictive biomarkers for poor response to AI. Lastly, novel MS identify additional non-HER2-E tumours not responding to AI with an increased risk of relapse. FUNDING: Cancer Research UK (CRUK/07/015).

Impact of Duration of Neoadjuvant Aromatase Inhibitors on Molecular Expression Profiles in Estrogen Receptor-positive Breast Cancers.

PURPOSE: Aromatase inhibitor (AI) treatment is the standard of care for postmenopausal women with primary estrogen receptor-positive breast cancer. The impact of duration of neoadjuvant endocrine therapy (NET) on molecular characteristics is still unknown. We evaluated and compared changes of gene expression profiles under short-term (2-week) versus longer-term neoadjuvant AIs. EXPERIMENTAL DESIGN: Global gene expression profiles from the PeriOperative Endocrine Therapy for Individualised Care (POETIC) trial (137 received 2 weeks of AIs and 47 received no treatment) and targeted gene expression from 80 patients with breast cancer treated with NET for more than 1 month (NeoAI) were assessed. Intrinsic subtyping, module scores covering different cancer pathways and immune-related genes were calculated for pretreated and posttreated tumors. RESULTS: The differences in intrinsic subtypes after NET were comparable between the two cohorts, with most Luminal B (90.0% in the POETIC trial and 76.3% in NeoAI) and 50.0% of HER2 enriched at baseline reclassified as Luminal A or normal-like after NET. Downregulation of proliferative-related pathways was observed after 2 weeks of AIs. However, more changes in genes from cancer-signaling pathways such as MAPK and PI3K/AKT/mTOR and immune response/immune-checkpoint components that were associated with AI-resistant tumors and differential outcome were observed in the NeoAI study. CONCLUSIONS: Tumor transcriptional profiles undergo bigger changes in response to longer NET. Changes in HER2-enriched and Luminal B subtypes are similar between the two cohorts, thus AI-sensitive intrinsic subtype tumors associated with good survival might be identified after 2 weeks of AI. The changes of immune-checkpoint component expression in early AI resistance and its impact on survival outcome warrants careful investigation in clinical trials.

Proteomic profiling of soft tissue sarcomas with SWATH mass spectrometry.

Soft tissue sarcomas (STS) are a group of rare and heterogeneous cancers. While large-scale genomic and epigenomic profiling of STS have been undertaken, proteomic analysis has thus far been limited. Here we utilise sequential window acquisition of all theoretical fragment ion spectra mass spectrometry (SWATH-MS) for proteomic profiling of formalin fixed paraffin embedded (FFPE) specimens from a cohort of STS patients (n = 36) across four histological subtypes (leiomyosarcoma, synovial sarcoma, undifferentiated pleomorphic sarcoma and dedifferentiated liposarcoma). We quantified 2951 proteins across all cases and show that there is a significant enrichment of gene sets associated with smooth muscle contraction in leiomyosarcoma, RNA splicing regulation in synovial sarcoma and leukocyte activation in undifferentiated pleomorphic sarcoma. We further identified a subgroup of STS cases that have a distinct expression profile in a panel of proteins, with worse survival outcomes when compared to the rest of the cohort. Our study highlights the value of comprehensive proteomic characterisation as a means to identify histotype-specific STS profiles that describe key biological pathways of clinical and therapeutic relevance; as well as for discovering new prognostic biomarkers in this group of rare and difficult-to-treat diseases.

Lights and Shadows in Immuno-Oncology Drug Development.

The rapidly evolving landscape of immuno-oncology (IO) is redefining the treatment of a number of cancer types. IO treatments are becoming increasingly complex, with different types of drugs emerging beyond checkpoint inhibitors. However, many of the new drugs either do not progress from phase I-II clinical trials or even fail in late-phase trials. We have identified at least five areas in the development of promising IO treatments that should be redefined for more efficient designs and accelerated approvals. Here we review those critical aspects of IO drug development that could be optimized for more successful outcome rates in all cancer types. It is important to focus our efforts on the mechanisms of action, types of response and adverse events of these novel agents. The use of appropriate clinical trial designs with robust biomarkers of response and surrogate endpoints will undoubtedly facilitate the development and subsequent approval of these drugs. Further research is also needed to establish biomarker-driven strategies to select which patients may benefit from immunotherapy and identify potential mechanisms of resistance.

3D Functional Genomics Screens Identify CREBBP as a Targetable Driver in Aggressive Triple-Negative Breast Cancer.

Triple-negative breast cancers (TNBC) are resistant to standard-of-care chemotherapy and lack known targetable driver gene alterations. Identification of novel drivers could aid the discovery of new treatment strategies for this hard-to-treat patient population, yet studies using high-throughput and accurate models to define the functions of driver genes in TNBC to date have been limited. Here, we employed unbiased functional genomics screening of the 200 most frequently mutated genes in breast cancer, using spheroid cultures to model in vivo-like conditions, and identified the histone acetyltransferase CREBBP as a novel tumor suppressor in TNBC. CREBBP protein expression in patient tumor samples was absent in 8% of TNBCs and at a high frequency in other tumors, including squamous lung cancer, where CREBBP-inactivating mutations are common. In TNBC, CREBBP alterations were associated with higher genomic heterogeneity and poorer patient survival and resulted in upregulation and dependency on a FOXM1 proliferative program. Targeting FOXM1-driven proliferation indirectly with clinical CDK4/6 inhibitors (CDK4/6i) selectively impaired growth in spheroids, cell line xenografts, and patient-derived models from multiple tumor types with CREBBP mutations or loss of protein expression. In conclusion, we have identified CREBBP as a novel driver in aggressive TNBC and identified an associated genetic vulnerability in tumor cells with alterations in CREBBP and provide a preclinical rationale for assessing CREBBP alterations as a biomarker of CDK4/6i response in a new patient population. SIGNIFICANCE: This study demonstrates that CREBBP genomic alterations drive aggressive TNBC, lung cancer, and lymphomas and may be selectively treated with clinical CDK4/6 inhibitors.

Intratumoral Transcriptome Heterogeneity Is Associated With Patient Prognosis and Sidedness in Patients With Colorectal Cancer Treated With Anti-EGFR Therapy From the CO.20 Trial.

PURPOSE: Metastatic colorectal cancers (mCRCs) assigned to the transit-amplifying (TA) CRCAssigner subtype are more sensitive to anti-epidermal growth factor receptor (EGFR) therapy. We evaluated the association between the intratumoral presence of TA signature (TA-high/TA-low, dubbed as TA-ness classification) and outcomes in CRCs treated with anti-EGFR therapy. PATIENTS AND METHODS: The TA-ness classes were defined in a discovery cohort (n = 84) and independently validated in a clinical trial (CO.20; cetuximab monotherapy arm; n = 121) and other samples using an established NanoString-based gene expression assay. Progression-free survival (PFS), overall survival (OS), and disease control rate (DCR) according to TA-ness classification were assessed by univariate and multivariate analyses. RESULTS: The TA-ness was measured in 772 samples from 712 patients. Patients (treated with anti-EGFR therapy) with TA-high tumors had significantly longer PFS (discovery hazard ratio [HR], 0.40; 95% CI, 0.25 to 0.64; P < .001; validation HR, 0.65; 95% CI, 0.45 to 0.93; P = .018), longer OS (discovery HR, 0.48; 95% CI, 0.29 to 0.78; P = .003; validation HR, 0.67; 95% CI, 0.46 to 0.98; P = .04), and higher DCR (discovery odds ratio [OR]; 14.8; 95% CI, 4.30 to 59.54; P < .001; validation OR, 4.35; 95% CI, 2.00 to 9.09; P < .001). TA-ness classification and its association with anti-EGFR therapy outcomes were further confirmed using publicly available data (n = 80) from metastatic samples (PFS P < .001) and patient-derived xenografts (P = .042). In an exploratory analysis of 55 patients with RAS/BRAF wild-type and left-sided tumors, TA-high class was significantly associated with longer PFS and trend toward higher response rate (PFS HR, 0.53; 95% CI, 0.28 to 1.00; P = .049; OR, 5.88; 95% CI, 0.71 to 4.55; P = .09; response rate 33% in TA-high and 7.7% in TA-low). CONCLUSION: TA-ness classification is associated with prognosis in patients with mCRC treated with anti-EGFR therapy and may further help understanding the value of sidedness in patients with RAS/BRAF wild-type tumors.

Application of a risk-management framework for integration of stromal tumor-infiltrating lymphocytes in clinical trials.

Stromal tumor-infiltrating lymphocytes (sTILs) are a potential predictive biomarker for immunotherapy response in metastatic triple-negative breast cancer (TNBC). To incorporate sTILs into clinical trials and diagnostics, reliable assessment is essential. In this review, we propose a new concept, namely the implementation of a risk-management framework that enables the use of sTILs as a stratification factor in clinical trials. We present the design of a biomarker risk-mitigation workflow that can be applied to any biomarker incorporation in clinical trials. We demonstrate the implementation of this concept using sTILs as an integral biomarker in a single-center phase II immunotherapy trial for metastatic TNBC (TONIC trial, NCT02499367), using this workflow to mitigate risks of suboptimal inclusion of sTILs in this specific trial. In this review, we demonstrate that a web-based scoring platform can mitigate potential risk factors when including sTILs in clinical trials, and we argue that this framework can be applied for any future biomarker-driven clinical trial setting.

Early Enrichment of ESR1 Mutations and the Impact on Gene Expression in Presurgical Primary Breast Cancer Treated with Aromatase Inhibitors.

PURPOSE: To investigate the presence of ESR1 mutations in primary estrogen-receptor-positive (ER+) breast cancer treated with extended (>4 weeks) neoadjuvant (presurgical) aromatase inhibitor (NAI) therapy and to identify patients who may gain less benefit from aromatase inhibition (AI) alone based upon on-treatment changes in gene expression. EXPERIMENTAL DESIGN: We evaluated ER, progesterone receptor, and Ki67 by immunostaining, ESR1 mutations by droplet-digital PCR and expression of over 800 key breast cancer genes in paired pre- and post-NAI tumor samples from 87 ER+ breast cancer patients. RESULTS: Cell proliferation and estrogen-regulated genes (ERG) remained suppressed in most tumors indicative of persistent response to NAI. Enrichment of ESR1 mutations was found in five tumors and predominantly in patients receiving therapy for >6 months. ESR1-mutant tumors showed increased expression of ESR1 transcript and limited suppression of ERGs and proliferation-associated genes in response to NAI. ESR1 wild-type tumors with high residual proliferation (Ki67r ≥10%; 15/87 tumors) showed lower ESR1/ER expression pre- and post-therapy and lower ERGs. Tumors with ESR1 mutations or Ki67r ≥10% showed less inhibition of estrogen response, cell cycle, and E2F-target genes. CONCLUSIONS: Ligand-independent ER signaling, as a result of ESR1 mutation or reduced ER dependence, identified after extended NAI therapy, can guide early selection of patients who would benefit from combination therapy.

A Four-gene Decision Tree Signature Classification of Triple-negative Breast Cancer: Implications for Targeted Therapeutics.

The molecular complexity of triple-negative breast cancers (TNBCs) provides a challenge for patient management. We set out to characterize this heterogeneous disease by combining transcriptomics and genomics data, with the aim of revealing convergent pathway dependencies with the potential for treatment intervention. A Bayesian algorithm was used to integrate molecular profiles in two TNBC cohorts, followed by validation using five independent cohorts (n = 1,168), including three clinical trials. A four-gene decision tree signature was identified, which robustly classified TNBCs into six subtypes. All four genes in the signature (EXO1, TP53BP2, FOXM1, and RSU1) are associated with either genomic instability, malignant growth, or treatment response. One of the six subtypes, MC6, encompassed the largest proportion of tumors (∼50%) in early diagnosed TNBCs. In TNBC patients with metastatic disease, the MC6 proportion was reduced to 25%, and was independently associated with a higher response rate to platinum-based chemotherapy. In TNBC cell line data, platinum sensitivity was recapitulated, and a sensitivity to the inhibition of the phosphatase PPM1D was revealed. Molecularly, MC6-TNBCs displayed high levels of telomeric allelic imbalances, enrichment of CD4+ and CD8+ immune signatures, and reduced expression of genes negatively regulating the MAPK signaling pathway. These observations suggest that our integrative classification approach may identify TNBC patients with discernible and theoretically pharmacologically tractable features that merit further studies in prospective trials.

Prognostic Value of Intrinsic Subtypes in Hormone Receptor-Positive Metastatic Breast Cancer Treated With Letrozole With or Without Lapatinib.

IMPORTANCE: The value of the intrinsic subtypes of breast cancer (luminal A, luminal B, human epidermal growth factor receptor 2 [currently known as ERBB2, but referred to as HER2 in this study]-enriched, and basal-like) in the metastatic setting is currently unknown. OBJECTIVE: To evaluate the association of the intrinsic subtypes of breast cancer with outcome and/or benefit in hormone receptor (HR)-positive metastatic breast cancer. DESIGN, SETTING, AND PARTICIPANTS: Unplanned retrospective analysis of 821 tumor samples (85.7% primary and 14.3% metastatic) from the EGF30008 phase 3 clinical trial (NCT00073528), in which postmenopausal women with HR-positive invasive breast cancer and no prior therapy for advanced or metastatic disease were randomized to letrozole with or without lapatinib, an epidermal growth factor receptor (EGFR)/HER2 tyrosine kinase inhibitor. Tumor samples were classified into each subtype using the research-based PAM50 classifier. Prior neoadjuvant/adjuvant antiestrogen therapy was allowed. Patients with extensive symptomatic visceral disease were excluded. Treatment effects were evaluated using interaction tests. MAIN OUTCOMES AND MEASURES: Primary and secondary end points were progression-free survival and overall survival. RESULTS: The median (range) age was 62 (31-94) years. Intrinsic subtype was the strongest prognostic factor independently associated with progression-free survival and overall survival in all patients, and in patients with HER2-negative (n = 644) or HER2-positive (n = 157) diseases. Median progression-free survival differed across the intrinsic subtypes of clinically HER2-negative disease: luminal A (16.9 [95% CI, 14.1-19.9] months), luminal B (11.0 [95% CI, 9.6-13.6] months), HER2-enriched (4.7 [95% CI, 2.7-10.8] months), and basal-like (4.1 [95% CI, 2.5-13.8] months). Median OS also differed across the intrinsic subtypes: luminal A (45 [95% CI, 41-not applicable {NA}] months), luminal B (37 [95% CI, 31-42] months), HER2-enriched (16 [95% CI, 10-NA] months), and basal-like (23 [95% CI, 12-NA] months). Patients with HER2-negative/HER2-enriched disease benefited from lapatinib therapy (median PFS, 6.49 vs 2.60 months; progression-free survival hazard ratio, 0.238 [95% CI, 0.066-0.863]; interaction P = .02). CONCLUSIONS AND RELEVANCE: This is the first study to reveal an association between intrinsic subtype and outcome in first-line HR-positive metastatic breast cancer. Patients with HR-positive/HER2-negative disease with a HER2-enriched profile may benefit from lapatinib in combination with endocrine therapy. The clinical value of intrinsic subtyping in hormone receptor-positive metastatic breast cancer warrants further investigation, but patients with luminal A/HER2-negative metastatic breast cancer might be good candidates for letrozole monotherapy in the first-line setting regardless of visceral disease and number of metastases.

Response and survival of breast cancer intrinsic subtypes following multi-agent neoadjuvant chemotherapy.

BACKGROUND: Predicting treatment benefit and/or outcome before any therapeutic intervention has taken place would be clinically very useful. Herein, we evaluate the ability of the intrinsic subtypes and the risk of relapse score at diagnosis to predict survival and response following neoadjuvant chemotherapy. In addition, we evaluated the ability of the Claudin-low and 7-TNBCtype classifications to predict response within triple-negative breast cancer (TNBC). METHODS: Gene expression and clinical-pathological data were evaluated in a combined dataset of 957 breast cancer patients, including 350 with TNBC, treated with sequential anthracycline and anti-microtubule-based neoadjuvant regimens. Intrinsic subtype, risk of relapse score based on subtype and proliferation (ROR-P), the Claudin-low subtype and the 7-TNBCtype subtype classification were evaluated. Logistic regression models for pathological complete response (pCR) and Cox models for distant relapse-free survival (DRFS) were used. RESULTS: Basal-like, Luminal A, Luminal B, and HER2-enriched subtypes represented 32.7%, 30.6%, 18.2%, and 10.3% of cases, respectively. Intrinsic subtype was independently associated with pCR in all patients, in hormone receptor-positive/HER2-negative disease, in HER2-positive disease, and in TNBC. The pCR rate of Basal-like disease was >35% across all clinical cohorts. Neither the Claudin-low nor the 7-TNBCtype subtype classifications predicted pCR within TNBCs after accounting for intrinsic subtype. Finally, intrinsic subtype and ROR-P provided independent prognostic information beyond clinicopathological variables and type of pathological response. A 5-year DRFS of 97.5% (92.8-100.0%) was observed in these neoadjuvant-treated and clinically node-negative patients predicted to be low risk by ROR-P (i.e. 57.4% of Luminal A tumors with clinically node-negative disease). CONCLUSIONS: Intrinsic subtyping at diagnosis provides prognostic and predictive information for patients receiving neoadjuvant chemotherapy. Although we could not exclude a survival benefit of neoadjuvant chemotherapy in patients with early breast cancer with clinically node-negative and ROR-low disease at diagnosis, the absolute benefit of cytotoxic therapy in this group might be rather small (if any).

Defining breast cancer intrinsic subtypes by quantitative receptor expression.

PURPOSE: To determine intrinsic breast cancer subtypes represented within categories defined by quantitative hormone receptor (HR) and HER2 expression. METHODS: We merged 1,557 cases from three randomized phase III trials into a single data set. These breast tumors were centrally reviewed in each trial for quantitative ER, PR, and HER2 expression by immunohistochemistry (IHC) stain and by reverse transcription-quantitative polymerase chain reaction (RT-qPCR), with intrinsic subtyping by research-based PAM50 RT-qPCR assay. RESULTS: Among 283 HER2-negative tumors with <1% HR expression by IHC, 207 (73%) were basal-like; other subtypes, particularly HER2-enriched (48, 17%), were present. Among the 1,298 HER2-negative tumors, borderline HR (1%-9% staining) was uncommon (n = 39), and these tumors were heterogeneous: 17 (44%) luminal A/B, 12 (31%) HER2-enriched, and only 7 (18%) basal-like. Including them in the definition of triple-negative breast cancer significantly diminished enrichment for basal-like cancer (p < .05). Among 106 HER2-positive tumors with <1% HR expression by IHC, the HER2-enriched subtype was the most frequent (87, 82%), whereas among 127 HER2-positive tumors with strong HR (>10%) expression, only 69 (54%) were HER2-enriched and 55 (43%) were luminal (39 luminal B, 16 luminal A). Quantitative HR expression by RT-qPCR gave similar results. Regardless of methodology, basal-like cases seldom expressed ER/ESR1 or PR/PGR and were associated with the lowest expression level of HER2/ERBB2 relative to other subtypes. CONCLUSION: Significant discordance remains between clinical assay-defined subsets and intrinsic subtype. For identifying basal-like breast cancer, the optimal HR IHC cut point was <1%, matching the American Society of Clinical Oncology and College of American Pathologists guidelines. Tumors with borderline HR staining are molecularly diverse and may require additional assays to clarify underlying biology.

Genomic Complexity Profiling Reveals That HORMAD1 Overexpression Contributes to Homologous Recombination Deficiency in Triple-Negative Breast Cancers.

UNLABELLED: Triple-negative breast cancers (TNBC) are characterized by a wide spectrum of genomic alterations, some of which might be caused by defects in DNA repair processes such as homologous recombination (HR). Despite this understanding, associating particular patterns of genomic instability with response to therapy has been challenging. Here, we show that allelic-imbalanced copy-number aberrations (AiCNA) are more prevalent in TNBCs that respond to platinum-based chemotherapy, thus providing a candidate predictive biomarker for this disease. Furthermore, we show that a high level of AiCNA is linked with elevated expression of a meiosis-associated gene, HORMAD1. Elevated HORMAD1 expression suppresses RAD51-dependent HR and drives the use of alternative forms of DNA repair, the generation of AiCNAs, as well as sensitizing cancer cells to HR-targeting therapies. Our data therefore provide a mechanistic association between HORMAD1 expression, a specific pattern of genomic instability, and an association with response to platinum-based chemotherapy in TNBC. SIGNIFICANCE: Previous studies have shown correlation between mutational "scars" and sensitivity to platinums extending beyond associations with BRCA1/2 mutation, but do not elucidate the mechanism. Here, a novel allele-specific copy-number characterization of genome instability identifies and functionally validates the inappropriate expression of the meiotic gene HORMAD1 as a driver of HR deficiency in TNBC, acting to induce allelic imbalance and moderate platinum and PARP inhibitor sensitivity with implications for the use of such "scars" and expression of meiotic genes as predictive biomarkers.

αB-crystallin Expression in Breast Cancer is Associated with Brain Metastasis.

BACKGROUND/OBJECTIVES: The molecular chaperone αB-crystallin is expressed in estrogen receptor, progesterone receptor and human epidermal growth factor receptor-2 "triple-negative" breast carcinomas and promotes brain and lung metastasis. We examined αB-crystallin expression in primary breast carcinomas with metastatic data to evaluate its association with prognosis and site-specific metastases. METHODS: αB-crystallin gene (CRYAB) expression was examined using publically available global-gene expression data (n=855 breast tumors) with first site of distant metastasis information ("855Met"). αB-crystallin protein expression was determined by immunohistochemistry using the clinically annotated tissue microarray (n=3987 breast tumors) from British Columbia Cancer Agency (BCCA). Kaplan-Meier and multivariable Cox regression analyses were used to evaluate the prognostic value of αB-crystallin. Multivariable logistic regression analysis was used to evaluate risks of αB-crystallin and other markers for site of metastasis. RESULTS: In the 855Met dataset, αB-crystallin gene (CRYAB) expression was an independent predictor of brain as the first distant site of relapse (HR = 1.2, (95% CI 1.0-1.4), P = 0.021). In the BCCA series, αB-crystallin protein expression was an independent prognostic marker of poor breast cancer specific survival (HR = 1.3, (95% CI 1.1-1.6), P = 0.014). Among patients with metastases, αB-crystallin was the strongest independent predictor of brain metastasis (OR = 2.99 (95% CI 1.83-4.89), P < 0.0001) and the only independent predictor of brain as the first site of distant metastasis (OR = 3.15 (95% CI1.43-6.95), P = 0.005). αB-crystallin was also associated with worse survival (3.0 versus 4.7 months, P = 0.007). CONCLUSIONS: αB-crystallin is a promising biomarker to identify breast cancer patients at high risk for early relapse in the brain, independent of ER and HER2 status.

Radiation-induced gene signature predicts pathologic complete response to neoadjuvant chemotherapy in breast cancer patients.

The identification of biomarkers predictive of neoadjuvant chemotherapy response in breast cancer patients would be an important advancement in personalized cancer therapy. In this study, we hypothesized that due to similarities between radiation- and chemotherapy-induced cellular response mechanisms, radiation-responsive genes may be useful in predicting response to neoadjuvant chemotherapy. Murine p53 null breast cancer cell lines representative of the luminal, basal-like and claudin-low human breast cancer subtypes were irradiated to identify radiation-responsive genes across subtypes. These murine tumor radiation-induced genes were then converted to their human orthologs, and subsequently tested as a predictor of pathologic complete response (pCR), which was validated on two independent published neoadjuvant chemotherapy datasets of genomic data with chemotherapy response. A radiation-induced gene signature consisting of 30 genes was identified on a training set of 337 human primary breast cancer tumor samples that was prognostic for survival. Mean expression of this signature was calculated for individual samples on two independent published datasets and was found to be significantly predictive of pCR. Multivariate logistic regression analysis in both independent datasets showed that this 30 gene signature added significant predictive information independent of that provided by standard clinical predictors and other gene expression-based predictors of pCR. This study provides new information for radiation-induced biology, as well as information regarding response to neoadjuvant chemotherapy and a possible means of improving the prediction of pCR.

Research-based PAM50 subtype predictor identifies higher responses and improved survival outcomes in HER2-positive breast cancer in the NOAH study.

PURPOSE: We report a retrospective exploratory analysis of the association of the research-based prediction analysis of microarray 50 (PAM50) subtype predictor with pathologic complete response (pCR) and event-free survival (EFS) in women enrolled in the NeOAdjuvant Herceptin (NOAH) trial. EXPERIMENTAL DESIGN: Gene expression profiling was performed using RNA from formalin-fixed paraffin-embedded core biopsies from 114 pretreated patients with HER2-positive (HER2(+)) tumors randomized to receive neoadjuvant doxorubicin/paclitaxel (AT) followed by cyclophosphamide/methotrexate/fluorouracil (CMF), or the same regimen in combination with trastuzumab for one year. A control cohort of 42 patients with HER2-negative tumors treated with AT-CMF was also included. The PAM50 subtypes, the PAM50 proliferation score, and the PAM50 risk of relapse score based on subtype (RORS) and subtype and proliferation (RORP) were evaluated. RESULTS: HER2-enriched (HER2-E) tumors predominated within HER2(+) disease, although all PAM50 intrinsic subtypes were identified across the three cohorts. The OR for achieving pCR with trastuzumab-based chemotherapy for HER2(+)/HER2-E and HER2(+)/RORP-high were 5.117 (P = 0.009) and 8.469 (P = 0.025), respectively, compared with chemotherapy only. The pCR rates of HER2(+)/HER2-E and HER2(+)/RORP-high after trastuzumab-based chemotherapy were 52.9% and 75.0%, respectively. A statistically nonsignificant trend was observed for more pronounced survival benefit with trastuzumab in patients with HER2(+)/HER2-E and HER2(+)/RORP-high tumors compared with patients with HER2(+)/non-HER2-E and HER2(+)/non-RORP-high tumors, respectively. CONCLUSIONS: As determined by EFS and pCR, patients with HER2(+)/HER2-E tumors, or HER2(+)/RORP-high tumors, benefit substantially from trastuzumab-based chemotherapy. The clinical value of this genomic test within HER2(+) disease warrants further investigation.

Predicting drug responsiveness in human cancers using genetically engineered mice.

PURPOSE: To use genetically engineered mouse models (GEMM) and orthotopic syngeneic murine transplants (OST) to develop gene expression-based predictors of response to anticancer drugs in human tumors. These mouse models offer advantages including precise genetics and an intact microenvironment/immune system. EXPERIMENTAL DESIGN: We examined the efficacy of 4 chemotherapeutic or targeted anticancer drugs, alone and in combination, using mouse models representing 3 distinct breast cancer subtypes: Basal-like (C3(1)-T-antigen GEMM), Luminal B (MMTV-Neu GEMM), and Claudin-low (T11/TP53-/- OST). We expression-profiled tumors to develop signatures that corresponded to treatment and response, and then tested their predictive potential using human patient data. RESULTS: Although a single agent exhibited exceptional efficacy (i.e., lapatinib in the Neu-driven model), generally single-agent activity was modest, whereas some combination therapies were more active and life prolonging. Through analysis of RNA expression in this large set of chemotherapy-treated murine tumors, we identified a pair of gene expression signatures that predicted pathologic complete response to neoadjuvant anthracycline/taxane therapy in human patients with breast cancer. CONCLUSIONS: These results show that murine-derived gene signatures can predict response even after accounting for common clinical variables and other predictive genomic signatures, suggesting that mice can be used to identify new biomarkers for human patients with cancer.