Pathway level alterations rather than mutations in single genes predict response to HER2-targeted therapies in the neo-ALTTO trial

Improving drug response prediction via integrating gene relationships with deep learning

Predicting the drug response of cancer cell lines is crucial for advancing personalized cancer treatment, yet remains challenging due to tumor heterogeneity and individual diversity. In this study, we present a deep learning-based framework named Deep neural network Integrating Prior Knowledge (DIPK) (DIPK), which adopts self-supervised techniques to integrate multiple valuable information, including gene interaction relationships, gene expression profiles and molecular topologies, to enhance prediction accuracy and robustness. We demonstrated the superior performance of DIPK compared to existing methods on both known and novel cells and drugs, underscoring the importance of gene interaction relationships in drug response prediction. In addition, DIPK extends its applicability to single-cell RNA sequencing data, showcasing its capability for single-cell-level response prediction and cell identification. Further, we assess the applicability of DIPK on clinical data. DIPK accurately predicted a higher response to paclitaxel in the pathological complete response (pCR) group compared to the residual disease group, affirming the better response of the pCR group to the chemotherapy compound. We believe that the integration of DIPK into clinical decision-making processes has the potential to enhance individualized treatment strategies for cancer patients.

Association of PIK3CA mutation with outcomes in HER2-positive breast cancer treated with anti-HER2 therapy: A meta-analysis and bioinformatic analysis of TCGA‑BRCA data

BACKGROUND

This study aimed to comprehensively explore the clinical significance of PIK3CA mutation in human epidermal growth factor receptor 2 (HER2)-positive breast cancer treated with anti-HER2 therapy.

METHODS

We systematically searched PubMed, Embase, and the Cochrane databases for eligible studies assessing the association between PIK3CA mutation and outcomes in patients with HER2-positive breast cancer receiving anti-HER2 therapy. The main outcomes included: (1) pathological complete response (pCR) or disease-free survival (DFS) for the neoadjuvant setting; (2) DFS or invasive DFS for the adjuvant setting; (3) objective response rate (ORR), progression-free survival (PFS), time-to-progression (TTP), or overall survival (OS) for the metastatic setting. The mutational landscape of HER2-positive breast cancer according to PIK3CA mutation status was examined based on TCGA breast cancer dataset.

RESULTS

Totally, 43 eligible studies, covering 11,099 patients with available data on PIK3CA mutation status, were identified. In the neoadjuvant setting, PIK3CA mutation was significantly associated with a lower pCR rate (OR=0.23, 95% CI 0.19-0.27, p<0.001). This association remained significant irrespective of the type of anti-HER2 therapy (single-agent or dual-agent) and hormone receptor status. There were no significant differences in DFS between PIK3CA mutated and wild-type patients in either the neoadjuvant or adjuvant settings. In the metastatic setting, PIK3CA mutation predicted worse ORR (OR=0.26, 95%CI 0.17-0.40, p<0.001), PFS (HR=1.28, 95%CI 1.03-1.59, p = 0.024) and TTP (HR=2.27, 95%CI 1.54-3.34, p<0.001). However, no significant association was observed between PIK3CA mutation status and OS. Distinct mutational landscapes were observed in HER2-positive breast cancer between individuals with PIK3CA mutations and those with wild-type PIK3CA.

CONCLUSIONS

PIK3CA mutation was significantly associated with a lower pCR rate in HER2-positive breast cancer treated with neoadjuvant anti-HER2 therapy. In the metastatic setting, PIK3CA mutation was predictive of worse ORR, PFS and TTP. These results suggest the potential for developing PI3K inhibitors as a therapeutic option for these patients.

Accelerated glycolysis in tumor microenvironment is associated with worse survival in triple-negative but not consistently with ER+/HER2- breast cancer

Metabolic reprogramming to sustain immortality is a hallmark of cancer and glycolysis is an important way to attain this. Thus, we investigate the association of glycolysis and associated pathways in the survival of breast cancer. A total of 5,176 breast cancer patients from multiple independent cohorts were analyzed. We determined the glycolytic signaling score by the degree of enrichment by Gene Set Variant Analysis and the median was used to divide each cohort into high vs low score groups. Glycolysis high breast cancer significantly enriched the hallmark cell proliferation-related gene sets (E2F targets, G2M checkpoint, and MYC targets v1 and v2) and was associated with high MKI67 expression. In all cohorts, triple-negative breast cancer (TNBC) was associated with the highest glycolysis score. It was found that in TNBC, glycolysis high breast cancer was associated with worse survival but in ER-positive/HER2-negative breast cancer this was not observed consistently. The glycolysis high TNBC enriched multiple pro-cancerous gene sets and was infiltrated with a low level of B-cells and anti-cancerous immune cells, and significantly associated with a decreased level of cytolytic activity. It was also observed that the glycolysis was higher in the metastatic sites than in the primary breast cancer and the survival was not affected by the metastatic sites. In conclusion, accelerated glycolysis is associated with cancer cell proliferation and worse survival in TNBC.

Genetic mutations in HER2-positive breast cancer: possible association with response to trastuzumab therapy

BACKGROUND

HER2-positive breast cancer occurs in 15-20% of breast cancer patients and is characterized by poor prognosis. Trastuzumab is considered the key drug for treatment of HER2-positive breast cancer patients. It improves patient survival; however, resistance to trastuzumab remains a challenge in HER2-positive breast cancer patients. Therefore, the prediction of response to trastuzumab is crucial to choose optimal treatment regimens. The aim of the study was to identify genetic variants that could predict response to anti-HER2-targeted therapy (trastuzumab) using next-generation sequencing.

METHOD

Genetic variants in the hotspot regions of 17 genes were studied in 24 Formalin-Fixed Paraffin-Embedded (FFPE) samples using Ion S5 next-generation sequencing system. FFPE samples were collected from HER2‑positive breast cancer patients previously treated with anti‑HER2‑targeted treatment (Trastuzumab). Patients were divided into two groups; trastuzumab-sensitive group and trastuzumab-resistant group based on their response to targeted therapy.

RESULTS

We identified 29 genetic variants in nine genes that only occurred in trastuzumab-resistant patients and could be associated with resistance to targeted therapy including TP53, ATM, RB1, MLH1, SMARCB1, SMO, GNAS, CDH1, and VHL. Four variants out of these 29 variants were repeated in more than one patient; two variants in TP53, one variant in ATM gene, and the last variant in RB1 gene. In addition, three genes were found to be mutated only in resistant patients; MLH1, SMARCB1 and SMO genes. Moreover, one novel allele (c.407A > G, p. Gln136Arg) was detected within exon 4 of TP53 gene in one resistant patient.

CONCLUSION

NGS sequencing is a useful tool to detect genetic variants that could predict response to trastuzumab therapy.

Discovering Synergistic Compounds with BYL-719 in PI3K Overactivated Basal-like PDXs

Basal-like triple-negative breast cancer (TNBC) tumor cells are difficult to eliminate due to resistance mechanisms that promote survival. While this breast cancer subtype has low PIK3CA mutation rates when compared to estrogen receptor-positive (ER+) breast cancers, most basal-like TNBCs have an overactive PI3K pathway due to gene amplification or high gene expression. BYL-719 is a PIK3CA inhibitor that has been found to have low drug-drug interactions, which increases the likelihood that it could be useful for combinatorial therapy. Alpelisib (BYL-719) with fulvestrant was recently approved for treating ER+ breast cancer patients whose cancer had developed resistance to ER-targeting therapy. In these studies, a set of basal-like patient-derived xenograft (PDX) models was transcriptionally defined with bulk and single-cell RNA-sequencing and clinically actionable mutation profiles defined with Oncomine mutational profiling. This information was overlaid onto therapeutic drug screening results. BYL-719-based, synergistic two-drug combinations were identified with 20 different compounds, including everolimus, afatinib, and dronedarone, which were also found to be effective at minimizing tumor growth. These data support the use of these drug combinations towards cancers with activating PIK3CA mutations/gene amplifications or PTEN deficient/PI3K overactive pathways.

Clinical implication of genetic composition and molecular mechanism on treatment strategies of HER2-positive breast cancers

The current clinical management model of HER2-positive breast cancers is commonly based on guidelines, which in turn are based on the design and outcome of clinical trials. While this model is useful to most practicing clinicians, the treatment outcome of individual patient is not certain at the start of treatment. As the understanding of the translational research of carcinogenesis and the related changes in cancer genetics and tumor microenvironment during treatment is critical in the selection of right choice of treatment to maximize the successful clinical outcome for the patient, this review article intends to discuss the latest developments in the genetic and molecular mechanisms of cancer progression and treatment resistance, and how they influence the planning of the treatment strategies of HER2-positive breast cancers.

Predictive biomarkers for molecularly targeted therapies and immunotherapies in breast cancer

Globally, breast cancer is the most common malignancy in women. Substantial efforts have been made to develop novel therapies, including targeted therapies and immunotherapies, for patients with breast cancer who do not respond to standard therapies. Consequently, new targeted therapies, such as cyclin-dependent kinase 4 and 6 inhibitors, poly (ADP-ribose) polymerase inhibitors, phosphoinositide 3-kinase inhibitor, and antibody-drug conjugates targeting human epidermal growth factor receptor 2 or trophoblast cell surface antigen-2, and immune checkpoint inhibitor targeting programmed cell death-1, have been developed and are now in clinical use. However, only some patients have benefited from these novel therapies; therefore, the identification and validation of reliable or more accurate biomarkers for predicting responses to these agents remain a major challenge. This review summarizes the currently available predictive biomarkers for breast cancer and describes recent efforts undertaken to identify potential predictive markers for molecularly targeted therapies and immune checkpoint inhibitors.

The applications of plasma cell-free DNA in cancer detection: Implications in the management of breast cancer patients

Liquid biopsy probes DNA, RNA, and proteins in body fluids for cancer detection and is one of the most rapidly developing areas in oncology. Tumor-derived DNA (circulating tumor DNA, ctDNA) in the context of cell-free DNA (cfDNA) in blood has been the main target for its potential utilities in cancer detection. Liquid biopsy can report tumor burden in real-time without invasive interventions, and would be feasible for screening tumor types that lack standard-of-care screening approaches. Two major approaches to interrogating ctDNA are genetic mutation and DNA methylation profiling. Mutation profiling can identify tumor driver mutations and guide precision therapy. Targeted genomic profiling of DNA methylation has become the main approach for cancer screening in the general population. Here we review the recent technological development and ongoing efforts in clinical applications. For clinical applications, we focus on breast cancer, in which subtype-specific biology demarcates the applications of ctDNA.

Unveiling the Potential of Liquid Biopsy in HER2-Positive Breast Cancer Management

Simple Summary

Breast cancer (BC) is the most prevailing cancer in women worldwide. Amongst the different BC subtypes, human epidermal growth factor receptor 2 (HER2)-positive tumours are characterised by an overexpression of the HER2 membrane receptor. Nowadays, HER2-status assessment relies on immunohistochemical methodologies in the tumour tissue, which could be complemented by novel methodologies to improve the clinical management of these patients. In this regard, liquid biopsy is an easy, rapid, and minimally invasive tool to obtain circulating tumour components from body fluids. Herein, by reviewing the published studies, we aim to decipher the clinical validity of liquid biopsy in both early and metastatic HER2-positive BC.

Abstract

Invasive breast cancer (BC) is the most common cancer in women with a slightly increasing yearly incidence. BC immunohistochemical characterisation is a crucial tool to define the intrinsic nature of each tumour and personalise BC patients’ clinical management. In this regard, the characterisation of human epidermal growth factor receptor 2 (HER2) status guides physicians to treat with therapies tailored to this membrane receptor. Standardly, a tumour solid biopsy is therefore required, which is an invasive procedure and has difficulties to provide the complete molecular picture of the tumour. To complement these standard-of-care approaches, liquid biopsy is a validated methodology to obtain circulating tumour components such as circulating tumour DNA (ctDNA) and circulating tumour cells (CTCs) from body fluids in an easy-to-perform minimal-invasive manner. However, its clinical validity in cancer is still to be demonstrated. This review focusses on the utilisation of both ctDNA and CTCs in early and metastatic HER2-positive BC tumours. We discuss recently published studies deciphering the capacity of liquid biopsy to determine the response to neoadjuvant and adjuvant therapies as well as to predict patients’ outcomes.

Can Systems Biology Advance Clinical Precision Oncology?

Precision oncology is perceived as a way forward to treat individual cancer patients. However, knowing particular cancer mutations is not enough for optimal therapeutic treatment, because cancer genotype-phenotype relationships are nonlinear and dynamic. Systems biology studies the biological processes at the systems' level, using an array of techniques, ranging from statistical methods to network reconstruction and analysis, to mathematical modeling. Its goal is to reconstruct the complex and often counterintuitive dynamic behavior of biological systems and quantitatively predict their responses to environmental perturbations. In this paper, we review the impact of systems biology on precision oncology. We show examples of how the analysis of signal transduction networks allows to dissect resistance to targeted therapies and inform the choice of combinations of targeted drugs based on tumor molecular alterations. Patient-specific biomarkers based on dynamical models of signaling networks can have a greater prognostic value than conventional biomarkers. These examples support systems biology models as valuable tools to advance clinical and translational oncological research.

Molecular Biomarkers Predict Pathological Complete Response of Neoadjuvant Chemotherapy in Breast Cancer Patients: Review

Simple Summary

Breast cancer is the most common cancer in women worldwide. Although many studies have aimed to understand the genetic basis of breast cancer, leading to increasingly accurate diagnoses, only a few molecular biomarkers are used in clinical practice to predict response to therapy. Current studies aim to develop more personalized therapies to decrease the adverse effects of chemotherapy. Personalized medicine not only requires clinical, but also molecular characterization of tumors, which allows the use of more effective drugs for each patient. The aim of this study was to identify potential molecular biomarkers that can predict the response to therapy after neoadjuvant chemotherapy in patients with breast cancer. In this review, we summarize genomic, transcriptomic, and proteomic biomarkers that can help predict the response to therapy.

Abstract

Neoadjuvant chemotherapy (NAC) is often used to treat locally advanced disease for tumor downstaging, thus improving the chances of breast-conserving surgery. From the NAC response, it is possible to obtain prognostic information as patients may reach a pathological complete response (pCR). Those who do might have significant advantages in terms of survival rates. Breast cancer (BC) is a heterogeneous disease that requires personalized treatment strategies. The development of targeted therapies depends on identifying biomarkers that can be used to assess treatment efficacy as well as the discovery of new and more accurate therapeutic agents. With the development of new “OMICS” technologies, i.e., genomics, transcriptomics, and proteomics, among others, the discovery of new biomarkers is increasingly being used in the context of clinical practice, bringing us closer to personalized management of BC treatment. The aim of this review is to compile the main biomarkers that predict pCR in BC after NAC.

Copy Number Aberration Analysis to Predict Response to Neoadjuvant Anti-HER2 Therapy: Results from the NeoALTTO Phase III Clinical Trial

PURPOSE

The heterogeneity of response to anti-HER2 agents represents a major challenge in patients with HER2-positive breast cancer. To better understand the sensitivity and resistance to trastuzumab and lapatinib, we investigated the role of copy number aberrations (CNA) in predicting pathologic complete response (pCR) and survival outcomes in the NeoALTTO trial.

EXPERIMENTAL DESIGN

The neoadjuvant phase III NeoALTTO trial enrolled 455 patients with HER2-positive early-stage breast cancer. DNA samples from 269 patients were assessed for genome-wide copy number profiling. Recurrent CNAs were found with GISTIC2.0.

RESULTS

CNA estimates were obtained for 184 patients included in NeoALTTO. Among those, matched transcriptome and whole-exome data were available for 154 and 181 patients, respectively. A significant association between gene copy number and pCR was demonstrated for ERBB2 amplification. Nevertheless, ERBB2 amplification ceased to be predictive once ERBB2 expression level was considered. GISTIC2.0 analysis revealed 159 recurrent CNA regions. Lower copy number levels of the 6q23-24 locus predicted absence of pCR in the whole cohort and in the estrogen receptor-positive subgroup. 6q23-24 deletion was significantly more frequent in TP53 wild-type (WT) compared with TP53-mutated, resulting in copy number levels significantly associated with lack of pCR only in the TP53 WT subgroup. Interestingly, a gene-ontology analysis highlighted several immune processes correlated to 6q23-24 copy number.

CONCLUSIONS

Our analysis identified ERBB2 copy number as well as 6q23-24 CNAs as predictors of response to anti-HER2-based treatment. ERBB2 expression outperformed ERBB2 amplification. The complexity of the 6q23-24 region warrants further investigation.

A novel five-gene score to predict complete pathological response to neoadjuvant chemotherapy in ER-positive/HER2-negative breast cancer

Neoadjuvant Chemotherapy (NAC) is not frequently used in ER-positive/HER2-negative breast cancer (BC) because around 10% patients achieve pathological complete response (pCR). Since NAC can result in cancer downstaging both in the breast and axilla and prevent a morbid surgery, thus a score to predict pCR in this population will be crucial to identify patients who can benefit from this approach. A total of 4038 patients from cohorts; GSE25066, GSE20194, Hess, GSE20181, TCGA-BRCA and METBRIC were analyzed. The score was generated by the 5 most highly expressed genes in the Hallmark E2F targets gene set amongst patients in the GSE25066 cohort with ER-positive/HER2-negative BC who achieved pCR. The area under the curve was significantly higher in the score than that for the E2F targets score. High score ER-positive/HER2-negative BCs were significantly associated with higher Nottingham pathological grade, AJCC cancer stage, MKI67 expression levels, intratumor heterogeneity, homologous recombination defects, mutation burden, neoantigen load, and infiltration of anti-cancer immune cells (CD4+, T helper type1, plasmacytoid dendritic cells, M1 macrophages). They also expressed lower abundance of stromal cells including fibroblasts, lymphatic endothelial cells, pericytes and adipocytes consistently in GSE25066, TCGA and METABRIC cohorts. All cell proliferation-related gene sets, G2M checkpoint, E2F targets, MYC targets v1 and v2, Mitotic Spindle, were strongly enriched in high score BCs consistently in 3 cohorts. The gene score was significantly associated with high pCR rate consistently in the GSE25066 (38%, P < 0.001), GSE20194 (16%, P = 0.006), and Hess cohort (23%, P = 0.037). In conclusion, the 5-gene score reflects cancer cell proliferation and immune cell infiltration, and predicts pCR after NAC in ER-positive/HER2-negative breast cancer.

Predicting breast cancer drug response using a multiple-layer cell line drug response network model

Background

Predicting patient drug response based on a patient’s molecular profile is one of the key goals of precision medicine in breast cancer (BC). Multiple drug response prediction models have been developed to address this problem. However, most of them were developed to make sensitivity predictions for multiple single drugs within cell lines from various cancer types instead of a single cancer type, do not take into account drug properties, and have not been validated in cancer patient-derived data. Among the multi-omics data, gene expression profiles have been shown to be the most informative data for drug response prediction. However, these models were often developed with individual genes. Therefore, this study aimed to develop a drug response prediction model for BC using multiple data types from both cell lines and drugs.

Methods

We first collected the baseline gene expression profiles of 49 BC cell lines along with IC₅₀ values for 220 drugs tested in these cell lines from Genomics of Drug Sensitivity in Cancer (GDSC). Using these data, we developed a multiple-layer cell line-drug response network (ML-CDN2) by integrating a one-layer cell line similarity network based on the pathway activity profiles and a three-layer drug similarity network based on the drug structures, targets, and pan-cancer IC₅₀ profiles. We further used ML-CDN2 to predict the drug response for new BC cell lines or patient-derived samples.

Results

ML-CDN2 demonstrated a good predictive performance, with the Pearson correlation coefficient between the observed and predicted IC₅₀ values for all GDSC cell line-drug pairs of 0.873. Also, ML-CDN2 showed a good performance when used to predict drug response in new BC cell lines from the Cancer Cell Line Encyclopedia (CCLE), with a Pearson correlation coefficient of 0.718. Moreover, we found that the cell line-derived ML-CDN2 model could be applied to predict drug response in the BC patient-derived samples from The Cancer Genome Atlas (TCGA).

Conclusions

The ML-CDN2 model was built to predict BC drug response using comprehensive information from both cell lines and drugs. Compared with existing methods, it has the potential to predict the drug response for BC patient-derived samples.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-021-08359-6.

Concurrent mutations associated with trastuzumab-resistance revealed by single cell sequencing

PURPOSE

HER2-positive breast cancer patients benefit from HER2-targeted therapies, among which the most commonly used is trastuzumab. However, acquired resistance typically happens within one year. The cellular heterogeneity of it is less clear.

METHODS

Here we generated trastuzumab-resistant cells in two HER2-positive breast cancer cell lines, SK-BR-3 and BT-474. Cells at different time points during the resistance induction were examined by exome sequencing to study changes of genomic alterations over time. Single cell-targeted sequencing was also used to identify resistance-associated concurrent mutations.

RESULTS

We found a rapid increase of copy number variation (CNV) regions and gradual accumulation of single nucleotide variations (SNVs). On the pathway level, non-synonymous SNVs for SK-BR-3 cells were enriched in the MAPK signaling pathway, while for BT-474 cells they were enriched in mTOR and PI3K-Akt signaling pathways. However, all of the three signaling pathways were in the downstream of the HER2 kinase. Putative trastuzumab-resistance-associated SNVs included AIFM1 P548L and ERBB2 M833R in SK-BR-3 cells, and ADAMTS19 V451L, OR5M9 D230N, COL9A1 R627T, and ITGA7 H911Q in BT-474 cells. Single-cell-targeted sequencing identified several concurrent mutations. By validation, we found that concurrent mutations (AIFM1 P548L and IL1RAPL2 S546C in SK-BR-3 cells, MFSD11 L242I and ANAPC4 E16K in BT-474 cells) led to a decrease of trastuzumab sensitivity.

CONCLUSION

Taken together, our study revealed a common pathway level trastuzumab-resistance mechanism for HER2-positive breast cancer cells. In addition, our identification of concurrent SNVs associated with trastuzumab-resistance may be indicative of potential targets for the treatment of trastuzumab-resistant breast cancer patients.

Nanobody-Based Quantification of GTP-Bound RHO Conformation Reveals RHOA and RHOC Activation Independent from Their Total Expression in Breast Cancer

As key regulators of the actin cytoskeleton, RHO GTPase expression and/or activity are deregulated in tumorigenesis and metastatic progression. Nevertheless, the vast majority of experiments supporting this conclusion was conducted on cell lines but not on human tumor samples that were mostly studied at the expression level only. Up to now, the activity of RHO proteins remains poorly investigated in human tumors. In this article, we present the development of a robust nanobody-based ELISA assay, with a high selectivity that allows an accurate quantification of RHO protein GTP-bound state in the nanomolar range (1 nM; 20 μg/L), not only in cell lines after treatment but also in tumor samples. Of note, we present here a fine analysis of RHOA-like and RAC1 active state in tumor samples with the most comprehensive study of RHOA-GTP and RHOC-GTP levels performed on human breast tumor samples. We revealed increased GTP-bound RHOA and RHOC protein activities in tumors compared to normal tissue counterparts, and demonstrated that the RHO active state and RHO expression are two independent parameters among different breast cancer subtypes. Our results further highlight the regulation of RHO protein activation in tumor samples and the relevance of directly studying RHO GTPase activities involvement in molecular pathways.

Identification of "regulation of RhoA activity panel" as a prognostic and predictive biomarker for gastric cancer

RhoA is a member of the RHO family GTPases and is associated with essential functions in gastric cancer. In this study, we identified a gastric cancer biomarker, termed the “regulation of RhoA activity panel” (RRAP). Patients with gastric cancer from The Cancer Genome Atlas database were divided into training (N=160) and validation (N=155) cohorts. A cohort of 109 Chinese gastric cancer patients was utilized as an independent validation. Patients with mutated RRAP showed significantly better overall survival than patients with wild type RRAP. We also analyzed the association between RRAP and the migration capacity, immune-related signatures, and the tumor microenvironment. RRAP-mutant tumors had a significantly lower degree of lymph node metastasis and lower activities of migration-related pathways. These tumors also showed significantly increased immune cell infiltration and cytotoxic activity. Furthermore, two independent patient cohorts who received immune checkpoint blockade therapy were assessed for RRAP mutant status. As expected, for both immunotherapy cohorts, higher response rates to immune checkpoint blockade therapy were observed in patients with RRAP-mutant tumors than in patients with wild type RRAP tumors. Overall, this study indicates that the RRAP gene set is a potential biomarker for gastric cancer prognosis and therapeutic selection.

Therapeutic Strategies for the Management of Hormone Receptor-Positive, Human Epidermal Growth Factor Receptor 2-Positive (HR+/HER2+) Breast Cancer: A Review of the Current Literature

Enormous advances have been made in the understanding and treatment of human epidermal growth factor receptor 2-positive breast cancer (HER2+ BC) in the last 30 years that have resulted in survival gains for affected patients. A growing body of evidence suggests that hormone receptor-positive (HR+)/HER2+ BC and HR-negative (HR-)/HER2+ BC are biologically different, with complex molecular bidirectional crosstalk between the estrogen receptor and HER2 pathway potentially affecting sensitivity to both HER2-targeted and endocrine therapy in patients with HR+/HER2+ BC. Subgroup analyses from trials enrolling patients with HER2+ BC and the results of clinical trials specifically designed to evaluate therapy in patients with HR+/HER2+ BC are helping to guide treatment decisions. In this context, encouraging results with strategies aimed at delaying or reversing drug resistance, including extended adjuvant therapy and the addition of drugs targeting alternative pathways, such as cyclin-dependent kinase (CDK) 4 and 6 inhibitors, have recently emerged. We have reached the point where tailoring the treatment according to risk and biology has become the paradigm in early BC. However, further clinical trials are needed that integrate translational research principles and identify and consider specific patient subgroups and biomarkers.

Evolving standards of care and new challenges in the management of HER2-positive breast cancer

The management of human epidermal growth factor receptor (HER2)-positive breast cancer (BC) has rapidly evolved over the last 20 years. Major advances have led to US Food and Drug Administration approval of 7 HER2-targeted therapies for the treatment of early-stage and/or advanced-stage disease. Although oncologic outcomes continue to improve, most patients with advanced HER2-positive BC ultimately die of their disease because of primary or acquired resistance to therapy, and patients with HER2-positive early BC who have residual invasive disease after preoperative systemic therapy are at a higher risk of distant recurrence and death. The concept of treatment de-escalation and escalation is increasingly important to optimally tailor therapy for patients with HER2-positive BC and is a major focus of the current review. Research efforts in this regard are discussed as well as updates regarding the evolving standard of care in the (neo)adjuvant and metastatic settings, including the use of novel combination therapies. The authors also briefly discuss ongoing challenges in the management of HER2-positive BC (eg, intrinsic vs acquired drug resistance, the identification of predictive biomarkers, the integration of imaging techniques to guide clinical practice), and the treatment of HER2-positive brain metastases. Research aimed at superseding these challenges will be imperative to ensure continued progress in the management of HER2-positive BC going forward.

Resistance to Neoadjuvant Treatment in Breast Cancer: Clinicopathological and Molecular Predictors

Neoadjuvant Chemotherapy (NAC) in Breast Cancer (BC) has proved useful for the reduction in tumor burden prior to surgery, allowing for a more extensive breast preservation and the eradication of subjacent micrometastases. However, the impact on prognosis is highly dependent on the establishment of Pathological Complete Response (pCR), in particular for Triple Negative (TN) and Hormonal Receptor negative/Human Epidermal growth factor Receptor 2 positive (HR-/HER2+) subtypes. Several pCR predictors, such as PAM50, Integrative Cluster (IntClust), mutations in PI3KCA, or the Trastuzumab Risk model (TRAR), are useful molecular tools for estimating response to treatment and are prognostic. Major evolution events during BC NAC that feature the Residual Disease (RD) are the loss of HR and HER2, which are prognostic of bad outcome, and stemness and immune depletion-related gene expression aberrations. This dynamic nature of the determinants of response to BC NAC, together with the extensive heterogeneity of BC, raises the need to discern the individual and subtype-specific determinants of resistance. Moreover, refining the current approaches for a comprehensive monitoring of tumor evolution during treatment, RD, and eventual recurrences is essential for identifying new actionable alterations and the integral best management of the disease.

The E2F Pathway Score as a Predictive Biomarker of Response to Neoadjuvant Therapy in ER+/HER2- Breast Cancer

E2F transcription factors play critical roles in the cell cycle. Therefore, their activity is expected to reflect tumor aggressiveness and responsiveness to therapy. We scored 3905 tumors of nine breast cancer cohorts for this activity based on their gene expression for the Hallmark E2F targets gene set. As expected, tumors with a high score had an increased expression of cell proliferation-related genes. A high score was significantly associated with shorter patient survival, greater MKI67 expression, histological grade, stage, and genomic aberrations. Furthermore, metastatic tumors had higher E2F scores than the primary tumors from which they arose. Although tumors with a high score had greater infiltration by both pro- and anti-cancerous immune cells, they had an increased expression of immune checkpoint genes. Estrogen receptor (ER)-positive/human epidermal growth factor receptor 2 (HER2)-negative cancer with a high E2F score achieved a significantly higher pathological complete response (pCR) rate to neoadjuvant chemotherapy. The E2F score was significantly associated with the expression of cyclin-dependent kinase (CDK)-related genes and strongly correlated with sensitivity to CDK inhibition in cell lines. In conclusion, the E2F score is a marker of breast cancer aggressiveness and predicts the responsiveness of ER-positive/HER2-negative patients to neoadjuvant chemotherapy and possibly to CDK and immune checkpoint inhibitors.

Advances in EGFR/HER2-directed clinical research on breast cancer

The discovery of the HER2 molecules has embarked a series of investigations on the efficacy and safety of different types of anti-HER2 therapies for treating breast cancer, with the clinical pathway requiring a more detailed, more precise, and more dynamics therapeutic approaches due to the heterogeneity of the disease. As the "do more" and "do less" approaches are becoming more important to personalize treatment for early HER2-positive breast cancer, recent advances aim at tackling the advanced stage of the disease by using novel therapeutic agents and combination strategies. There are also important points of consideration on prognosis and choice of therapies, including HER2 gene copy number, HER2 heterogeneity, tissue biomarkers, blood-based biomarkers, and HER2 mutation and its treatment. Altogether, these could potentially play a vital role in the journey of HER2-positive breast cancer patient to achieve greater survival benefit and potentially a cure for the disease.

G2M Cell Cycle Pathway Score as a Prognostic Biomarker of Metastasis in Estrogen Receptor (ER)-Positive Breast Cancer

The vast majority of breast cancer death is a result of metastasis. Thus, accurate identification of patients who are likely to have metastasis is expected to improve survival. The G2M checkpoint plays a critical role in cell cycle. We hypothesized that breast cancer tumors with high activity of G2M pathway genes are more aggressive and likely to metastasize. To test this, we used the single-sample gene set variation analysis method to calculate the score for the Hallmark G2M checkpoint pathway using gene expression data of a total of 4626 samples from 12 human breast cancer cohorts. As expected, a high G2M pathway score correlated with enriched tumor expression of other cell proliferation-related gene sets. The score was significantly associated with clinical aggressive features of tumors and patient survival in estrogen receptor (ER)-positive/human epidermal growth factor receptor 2 (HER2)-negative breast cancer. Interestingly, a high G2M score of metastasis tumors was also significantly associated with worse survival. In primary as well as metastasis tumors with high scores, the infiltration of both pro- and anti-cancerous immune cells increased. Tumor G2M score was also associated with treatment response to systemic chemotherapy in ER-positive/HER2-negative cancer, and was predictive of response to cyclin-dependent kinase inhibition therapy.

Analysis of Pre- and Posttreatment Tissues from the SWOG S0800 Trial Reveals an Effect of Neoadjuvant Chemotherapy on the Breast Cancer Genome

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 "G2-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.

Circulating tumor DNA clearance predicts prognosis across treatment regimen in a large real-world longitudinally monitored advanced non-small cell lung cancer cohort

Background

Although growth advantage of certain clones would ultimately translate into a clinically visible disease progression, radiological imaging does not reflect clonal evolution at molecular level. Circulating tumor DNA (ctDNA), validated as a tool for mutation detection in lung cancer, could reflect dynamic molecular changes. We evaluated the utility of ctDNA as a predictive and a prognostic marker in disease monitoring of advanced non-small cell lung cancer (NSCLC) patients.

Methods

This is a multicenter prospective cohort study. We performed capture-based ultra-deep sequencing on longitudinal plasma samples utilizing a panel consisting of 168 NSCLC-related genes on 949 advanced NSCLC patients with driver mutations to monitor treatment responses and disease progression. The correlations between ctDNA and progression-free survival (PFS)/overall survival (OS) were performed on 248 patients undergoing various treatments with the minimum of 2 ctDNA tests.

Results

The results of this study revealed that higher ctDNA abundance (P=0.012) and mutation count (P=8.5×10⁻⁴) at baseline are associated with shorter OS. We also found that patients with ctDNA clearance, not just driver mutation clearance, at any point during the course of treatment were associated with longer PFS (P=2.2×10⁻¹⁶, HR 0.28) and OS (P=4.5×10⁻⁶, HR 0.19) regardless of type of treatment and evaluation schedule.

Conclusions

This prospective real-world study shows that ctDNA clearance during treatment may serve as predictive and prognostic marker across a wide spectrum of treatment regimens.

Identification and clinical impact of potentially actionable somatic oncogenic mutations in solid tumor samples

BACKGROUND

An increasing number of anti-cancer therapeutic agents target specific mutant proteins that are expressed by many different tumor types. Successful use of these therapies is dependent on the presence or absence of somatic mutations within the patient's tumor that can confer clinical efficacy or drug resistance.

METHODS

The aim of our study was to determine the type, frequency, overlap and functional proteomic effects of potentially targetable recurrent somatic hotspot mutations in 47 cancer-related genes in multiple disease sites that could be potential therapeutic targets using currently available agents or agents in clinical development.

RESULTS

Using MassArray technology, of the 1300 patient tumors analysed 571 (43.9%) had at least one somatic mutation. Mutations were identified in 30 different genes. KRAS (16.5%), PIK3CA (13.6%) and BRAF (3.8%) were the most frequently mutated genes. Prostate (10.8%) had the lowest number of somatic mutations identified, while no mutations were identified in sarcoma. Ocular melanoma (90.6%), endometrial (72.4%) and colorectal (66.4%) tumors had the highest number of mutations. We noted high concordance between mutations in different parts of the tumor (94%) and matched primary and metastatic samples (90%). KRAS and BRAF mutations were mutually exclusive. Mutation co-occurrence involved mainly PIK3CA and PTPN11, and PTPN11 and APC. Reverse Phase Protein Array (RPPA) analysis demonstrated that PI3K and MAPK signalling pathways were more altered in tumors with mutations compared to wild type tumors.

CONCLUSIONS

Hotspot mutational profiling is a sensitive, high-throughput approach for identifying mutations of clinical relevance to molecular based therapeutics for treatment of cancer, and could potentially be of use in identifying novel opportunities for genotype-driven clinical trials.

An RB-1 loss of function gene signature as a tool to predict response to neoadjuvant chemotherapy plus anti-HER2 agents: a substudy of the NeoALTTO trial (BIG 1-06)

BACKGROUND

Chemotherapy added to anti-HER2 agents (H) is the treatment of choice in patients with HER2+ early breast cancer. However, HER2+ tumours are clinically and biologically heterogeneous, and treatment response varies significantly by hormone receptor (HR) status and molecular subtype. Predictive biomarkers are needed in this context. This study assessed whether an RB-1 loss of function gene signature (RBsig) is predictive of response to neoadjuvant chemotherapy in combination with trastuzumab, lapatinib or both, within the NeoALTTO trial.

METHODS

We collected RNA-sequencing data from pretreatment biopsies derived from the NeoALTTO trial. RBsig expression was computed retrospectively and correlated with pathological complete response (pCR) using receiver-operating characteristic (ROC) curves. The RBsig was dichotomised as High/Low in correspondence to the 25th percentile. Reported p values resulted from Fisher's exact test.

RESULTS

Of 455 NeoALTTO patients, 244 were eligible for this substudy (HR+ n = 129; HR- n = 115). Overall, pCR rate was significantly higher in patients with RBsig High tumours than those with RBsig Low (35% versus 18% respectively; p = 0.01). The area under the ROC curve (AUC) was 0.60 (95% CI 0.52-0.67). A remarkably low pCR rate of 11% was seen in HR+/RBsig Low patients versus 28% in HR+/RBsig High.

CONCLUSIONS

These results indicate RBsig may add valuable information to HER2 and HR expression, which may in turn inform treatment choices. HR+/HER2+/RBsig Low breast cancers exhibited the poorest pathological response following chemotherapy plus H. Accordingly, in such patients, endocrine therapy in combination with H and, possibly, a CDK4/6 inhibitor, may potentially prove to be a more effective treatment.

Reliability of Whole-Exome Sequencing for Assessing Intratumor Genetic Heterogeneity

Multi-region sequencing is used to detect intratumor genetic heterogeneity (ITGH) in tumors. To assess whether genuine ITGH can be distinguished from sequencing artifacts, we performed whole-exome sequencing (WES) on three anatomically distinct regions of the same tumor with technical replicates to estimate technical noise. Somatic variants were detected with three different WES pipelines and subsequently validated by high-depth amplicon sequencing. The cancer-only pipeline was unreliable, with about 69% of the identified somatic variants being false positive. Even with matched normal DNA for which 82% of the somatic variants were detected reliably, only 36%-78% were found consistently in technical replicate pairs. Overall, 34%-80% of the discordant somatic variants, which could be interpreted as ITGH, were found to constitute technical noise. Excluding mutations affecting low-mappability regions or occurring in certain mutational contexts was found to reduce artifacts, yet detection of subclonal mutations by WES in the absence of orthogonal validation remains unreliable.

Comutation of PIK3CA and TP53 in Residual Disease After Preoperative Anti-HER2 Therapy in ERBB2 (HER2)-Amplified Early Breast Cancer

PURPOSE

To identify proteomic and genomic alterations in residual disease (RD) for human epidermal growth factor receptor 2 (HER2)-positive (HER2+) breast cancer (BC) after preoperative trastuzumab (H), lapatinib (L), or both (H+L) in combination with chemotherapy.

PATIENTS AND METHODS

Patients with stage II/III HER2+ BC (n = 100) were randomly assigned to preoperative treatment with H versus L 1,250mg versus H+L (L: 750 to 1,000 mg) plus 5-fluorouracil, epirubicin, and cyclophosphamide, followed by weekly paclitaxel. After receiving institutional review board–approved informed consent, targeted next-generation sequencing was performed on 20 patients’ formalin-fixed paraffin embedded tumors to characterize genomic alterations across 287 cancer-related genes. Reverse phase protein array (RPPA) analysis was performed on both the baseline biopsy and RD specimens, when available.

RESULTS

Two of 20 RD tissues were HER2 negative per next-generation sequencing; one sample had insufficient tissue. Of six pretreatment biopsy specimens, four were comutated with TP53 and PIK3CA. Of 17 HER2+ RD, seven specimens (41%) had PIK3CA mutations always comutated with TP53, and four (24%) also had concurrent CDK12 amplification. Overall, CDK12 amplification was observed in eight of the 17 (47%) HER2+ RD specimens. A total of 12 RD specimens (71%) had TP53 mutations. Although prevalence of individual TP53 and PIK3CA mutations was only modestly higher than published estimates for those in HER2+ primary BCs (55% and 32% for TP53 and PIK3CA, respectively), prevalence of these as comutations appeared higher (41%), compared with less than 10% in several series. On RPPA analysis of the RD tissue with comutations, the strongest Spearman ρ correlations were limited to EGFR and phospho-AKT (ρ, 0.999; P = .019) and phospho-mTOR and phospho-S6 ribosomal protein (ρ, 0.994; P = .048).

CONCLUSION

HER2-amplified RD tissue after preoperative H, L, or H+L plus chemotherapy was enriched for PIK3CA and TP53 comutations, and the RD tissue demonstrated activation of EGFR/AKT/mTOR signaling on RPPA.

Sequential somatic mutations upon secondary anti-HER2 treatment resistance in metastatic ERBB2S310F mutated extramammary Paget's disease

Metastatic extramammary Paget’s disease is a rare adenocarcinoma with poor prognosis. Several reports of human epidermal growth factor receptor 2 alterations point to its pathogenic role in the disease. However, the occurrence of treatment resistance to anti-HER2 therapy demand the need for further knowledge. We report of a patient with metastatic penoscrotal extramammary Paget’s disease, with an ERBB2^S310F mutation, in which near complete response was achieved upon treatment with trastuzumab and carboplatin. However, after 10 cycles of trastuzumab and carboplatin, widespread metastasis re-occurred. Analysis of a newly developing metastasis revealed additional genomic alterations including ERBB3^A232V and PIK3CA^G106V point mutations as well as MET and CDK6 amplification, providing a potential mechanism of acquired treatment resistance. Therefore, ERBB family inhibitor afatinib was initiated. Unfortunately, the patient succumbed to disease-related complications shortly after treatment initiation. This is the first report of ERBB2^S310F mutated, metastatic extramammary Paget’s disease with secondary resistance to trastuzumab / carboplatin, potentially due to additional acquired genomic alterations. This case contributes to the growing evidence of HER2 in the pathogenesis of metastatic extramammary Paget’s disease and emphasizes the importance of repetitive, genomic analysis in rare diseases.

Emergence of ERBB2 Mutation as a Biomarker and an Actionable Target in Solid Cancers

The oncogenic role ERBB2 amplification is well established in breast and gastric cancers. This has led to the development of a well-known portfolio of monoclonal antibodies and kinase inhibitors targeting the ERBB2 kinase. More recently, activating mutations in the ERBB2 gene have been increasingly reported in multiple solid cancers and were shown to play an oncogenic role similar to that of ERBB2 amplification. Thus, ERBB2 mutations define a distinct molecular subtype of solid tumors and serve as actionable targets. However, efforts to target ERBB2 mutation has met with limited clinical success, possibly because of their low frequency, inadequate understanding of the biological activity of these mutations, and difficulty in separating the drivers from the passenger mutations. Given the current impetus to deliver molecularly targeted treatments for cancer, there is an important need to understand the therapeutic potential of ERBB2 mutations. Here we review the distribution of ERBB2 mutations in different tumor types, their potential as a novel biomarker that defines new subsets in many cancers, and current data on preclinical and clinical efforts to target these mutations. IMPLICATIONS FOR PRACTICE: A current trend in oncology is to identify novel genomic drivers of solid tumors and developing precision treatments that target them. ERBB2 amplification is an established therapeutic target in breast and gastric cancers, but efforts to translate this finding to other solid tumors with ERBB2 amplification have not been effective. Recently the focus has turned to targeting activating ERBB2 mutations. The year 2018 marked an important milestone in establishing ERBB2 mutation as an important actionable target in multiple cancer types. There have been several recent preclinical and clinical studies evaluating ERBB2 mutation as a therapeutic target with varying success. With increasing access to next-generation sequencing technologies in the clinic, oncologists are frequently identifying activating ERBB2 mutations in patients with cancer. There is a significant need both from the clinician and bench scientist perspectives to understand the current state of affairs for ERBB2 mutations.

Mutational Diversity and Therapy Response in Breast Cancer: A Sequencing Analysis in the Neoadjuvant GeparSepto Trial

PURPOSE

Next-generation sequencing (NGS) can be used for comprehensive investigation of molecular events in breast cancer. We evaluated the relevance of genomic alterations for response to neoadjuvant chemotherapy (NACT) in the GeparSepto trial.

EXPERIMENTAL DESIGN

Eight hundred fifty-one pretherapeutic formalin-fixed paraffin-embedded (FFPE) core biopsies from GeparSepto study were sequenced. The panel included 16 genes for mutational (AKT1, BRAF, CDH1, EGFR, ERBB2, ESR1, FBXW7, FGFR2, HRAS, KRAS, NRAS, SF3B1, TP53, HNF1A, PIK3CA, and PTEN) and 8 genes for copy-number alteration analysis (CCND1, ERBB2, FGFR1, PAK1, PIK3CA, TOP2A, TP53, and ZNF703).

RESULTS

The most common genomic alterations were mutations of TP53 (38.4%) and PIK3CA (21.5%), and 8 different amplifications (TOP2A 34.9%; ERBB2 30.6%; ZNF703 30.1%; TP53 21.9%; PIK3CA 24.1%; CCND1 17.7%; PAK1 14.9%; FGFR 12.6%). All other alterations had a prevalence of less than 5%. The genetic heterogeneity in different breast cancer subtypes [lum/HER2neg vs. HER2pos vs. triple-negative breast cancer (TNBC)] was significantly linked to differences in NACT response. A significantly reduced pathologic complete response rate was observed in PIK3CA-mutated breast cancer [PIK3CAmut: 23.0% vs. wild-type (wt) 38.8%, P < 0.0001] in particular in the HER2pos subcohort [multivariate OR = 0.43 (95% CI, 0.24-0.79), P = 0.006]. An increased response to nab-paclitaxel was observed only in PIK3CAwt breast cancer, with univariate significance for the complete cohort (P = 0.009) and the TNBC (P = 0.013) and multivariate significance in the HER2pos subcohort (test for interaction P = 0.0074).

CONCLUSIONS

High genetic heterogeneity was observed in different breast cancer subtypes. Our study shows that FFPE-based NGS can be used to identify markers of therapy resistance in clinical study cohorts. PIK3CA mutations could be a major mediator of therapy resistance in breast cancer.

Circulating Tumor DNA in HER2-Amplified Breast Cancer: A Translational Research Substudy of the NeoALTTO Phase III Trial

PURPOSE

In the neoadjuvant treatment (NAT) setting, dual HER2-targeted therapy is associated with increased pathologic complete response (pCR) rates compared with each therapy alone. Biomarkers allowing to predict treatment response during NAT are needed. We aim to evaluate whether circulating tumor DNA (ctDNA) is associated with response to anti-HER2-targeted therapy.

EXPERIMENTAL DESIGN

Plasma DNA collected before NAT, at week 2, and before surgery from patients enrolled in the NeoALTTO trial was assessed using digital PCR for PIK3CA and TP53 mutation detection.

RESULTS

A total of 69 of 455 (15.2%) patients had a PIK3CA and/or TP53 mutation detected in the baseline tumor sample and evaluable ctDNA results from baseline samples. CtDNA was detected in 41%, 20%, and 5% patients before NAT, at week 2, and before surgery, respectively. ctDNA detection before NAT was significantly associated with older age and ER-negative status. ctDNA detection before NAT was associated with decreased odds of achieving pCR (OR = 0.15; 95% CI, 0.034-0.7; P = 0.0089), but not with event-free survival (EFS). Analyses for EFS were underpowered. Interestingly, the patients with HER2-enriched subtype tumors and undetectable ctDNA at baseline had the highest pCR rates. In contrast, patients with persistent ctDNA detection at baseline and week 2 had the lowest rate of pCR.

CONCLUSIONS

ctDNA detection before neoadjuvant anti-HER2 therapies is associated with decreased pCR rates. Interestingly, patients with HER2-enriched tumors and undetectable ctDNA at baseline had the highest pCR rates, therefore appearing as the best candidates for treatment deescalation strategies.

Tailored NEOadjuvant epirubicin, cyclophosphamide and Nanoparticle Albumin-Bound paclitaxel for breast cancer: The phase II NEONAB trial-Clinical outcomes and molecular determinants of response

BACKGROUND

This study evaluated the feasibility of achieving high response rates in stage II or III breast cancer by tailoring neoadjuvant therapy using clinical and histopathological features and the Oncotype DX Breast Recurrence Score. Genomic determinants of response and resistance were also explored.

PATIENTS AND OUTCOME MEASURES

Fifty-one patients were enrolled. The primary cohort comprised 40 patients: 15 human epidermal growth factor receptor type 2 (HER2)-amplified; 15 triple-negative (TNBC); and ten hormone receptor (HR)-positive, HER2-non-amplified tumours; with recurrence scores ≥25. Patients were treated with epirubicin and cyclophosphamide, followed by nab-paclitaxel, with the addition of trastuzumab if HER2-amplified. The primary endpoint was pathological complete response (pCR) in the breast. Pre- and post-treatment tumour samples underwent variant burden, gene and gene pathway, mutational signature profile and clonal evolution analyses.

RESULTS

The pCR rates were: overall 55% (n = 22), HER2-amplified 80% (n = 12), triple-negative 46% (n = 7) and HR-positive, HER2-non-amplified 30% (n = 3). Grade 3 or 4 adverse events included febrile neutropenia (8%), neutropenia (18%), sensory neuropathy (5%), deranged transaminases (5%), fatigue (2%), diarrhoea (2%), and pneumothorax (2%). Molecular analyses demonstrated strong similarities between residual disease and matched primary tumour. ATM signalling pathway alterations and the presence of a COSMIC Signature 3 implied the majority of tumours contained some form of homologous repair deficiency. ATM pathway alterations were identified in the subset of TNBC patients who did not achieve pCR; Signature 3 was present in both pCR and non-pCR subgroups. Clonal evolution analyses demonstrated both persistence and emergence of chemoresistant clones.

CONCLUSIONS

This treatment regime resulted in a high rate of pCR, demonstrating that tailored neoadjuvant therapy using a genomic recurrence score is feasible and warrants further investigation. Molecular analysis revealed few commonalities between patients. For TNBC future clinical gains will require precision medicine, potentially using DNA sequencing to identify specific targets for individuals with resistant disease.

TRIAL REGISTRATION

Clinicaltrials.gov NCT01830244.

Predict drug sensitivity of cancer cells with pathway activity inference

Background

Predicting cellular responses to drugs has been a major challenge for personalized drug therapy regimen. Recent pharmacogenomic studies measured the sensitivities of heterogeneous cell lines to numerous drugs, and provided valuable data resources to develop and validate computational approaches for the prediction of drug responses. Most of current approaches predict drug sensitivity by building prediction models with individual genes, which suffer from low reproducibility due to biologic variability and difficulty to interpret biological relevance of novel gene-drug associations. As an alternative, pathway activity scores derived from gene expression could predict drug response of cancer cells.

Method

In this study, pathway-based prediction models were built with four approaches inferring pathway activity in unsupervised manner, including competitive scoring approaches (DiffRank and GSVA) and self-contained scoring approaches (PLAGE and Z-score). These unsupervised pathway activity inference approaches were applied to predict drug responses of cancer cells using data from Cancer Cell Line Encyclopedia (CCLE).

Results

Our analysis on all the 24 drugs from CCLE demonstrated that pathway-based models achieved better predictions for 14 out of the 24 drugs, while taking fewer features as inputs. Further investigation on indicated that pathway-based models indeed captured pathways involving drug-related genes (targets, transporters and metabolic enzymes) for majority of drugs, whereas gene-models failed to identify these drug-related genes, in most cases. Among the four approaches, competitive scoring (DiffRank and GSVA) provided more accurate predictions and captured more pathways involving drug-related genes than self-contained scoring (PLAGE and Z-Score). Detailed interpretation of top pathways from the top method (DiffRank) highlights the merit of pathway-based approaches to predict drug response by identifying pathways relevant to drug mechanisms.

Conclusion

Taken together, pathway-based modeling with inferred pathway activity is a promising alternative to predict drug response, with the ability to easily interpret results and provide biological insights into the mechanisms of drug actions.

Electronic supplementary material

The online version of this article (10.1186/s12920-018-0449-4) contains supplementary material, which is available to authorized users.

Immunohistochemistry-Enabled Precision Medicine

Immunohistochemistry (IHC) can be applied to diagnostic aspects of pathologic examination to provide aid in assignment of lineage and histologic type of cancer. Increasingly, however, IHC is widely used to provide prognostic and predictive (theranostic) information about the neoplastic disease. A refinement of theranostic application of IHC can be seen in the use of "genomic probing" where antibody staining results are directly correlated with an underlying genetic alteration in the tumor (somatic mutations) and/or the patient (germline constitution). All these aspects of IHC find their best use in guiding the oncologists in the optimal use of therapy for the patients.

Integrated Analysis of RNA and DNA from the Phase III Trial CALGB 40601 Identifies Predictors of Response to Trastuzumab-Based Neoadjuvant Chemotherapy in HER2-Positive Breast Cancer

Purpose: Response to a complex trastuzumab-based regimen is affected by multiple features of the tumor and its microenvironment. Developing a predictive algorithm is key to optimizing HER2-targeting therapy.Experimental Design: We analyzed 137 pretreatment tumors with mRNA-seq and DNA exome sequencing from CALGB 40601, a neoadjuvant phase III trial of paclitaxel plus trastuzumab with or without lapatinib in stage II to III HER2-positive breast cancer. We adopted an Elastic Net regularized regression approach that controls for covarying features within high-dimensional data. First, we applied 517 known gene expression signatures to develop an Elastic Net model to predict pCR, which we validated on 143 samples from four independent trials. Next, we performed integrative analyses incorporating clinicopathologic information with somatic mutation status, DNA copy number alterations (CNA), and gene signatures.Results: The Elastic Net model using only gene signatures predicted pCR in the validation sets (AUC = 0.76). Integrative analyses showed that models containing gene signatures, clinical features, and DNA information were better pCR predictors than models containing a single data type. Frequently selected variables from the multiplatform models included amplifications of chromosome 6p, TP53 mutation, HER2-enriched subtype, and immune signatures. Variables predicting resistance included Luminal/ER⁺ features.Conclusions: Models using RNA only, as well as integrated RNA and DNA models, can predict pCR with improved accuracy over clinical variables. Somatic DNA alterations (mutation, CNAs), tumor molecular subtype (HER2E, Luminal), and the microenvironment (immune cells) were independent predictors of response to trastuzumab and paclitaxel-based regimens. This highlights the complexity of predicting response in HER2-positive breast cancer. Clin Cancer Res; 24(21); 5292-304. ©2018 AACR.

Pathway-based dissection of the genomic heterogeneity of cancer hallmarks' acquisition with SLAPenrich

Cancer hallmarks are evolutionary traits required by a tumour to develop. While extensively characterised, the way these traits are achieved through the accumulation of somatic mutations in key biological pathways is not fully understood. To shed light on this subject, we characterised the landscape of pathway alterations associated with somatic mutations observed in 4,415 patients across ten cancer types, using 374 orthogonal pathway gene-sets mapped onto canonical cancer hallmarks. Towards this end, we developed SLAPenrich: a computational method based on population-level statistics, freely available as an open source R package. Assembling the identified pathway alterations into sets of hallmark signatures allowed us to connect somatic mutations to clinically interpretable cancer mechanisms. Further, we explored the heterogeneity of these signatures, in terms of ratio of altered pathways associated with each individual hallmark, assuming that this is reflective of the extent of selective advantage provided to the cancer type under consideration. Our analysis revealed the predominance of certain hallmarks in specific cancer types, thus suggesting different evolutionary trajectories across cancer lineages. Finally, although many pathway alteration enrichments are guided by somatic mutations in frequently altered high-confidence cancer genes, excluding these driver mutations preserves the hallmark heterogeneity signatures, thus the detected hallmarks' predominance across cancer types. As a consequence, we propose the hallmark signatures as a ground truth to characterise tails of infrequent genomic alterations and identify potential novel cancer driver genes and networks.

Genomic characterization of HER2-positive breast cancer and response to neoadjuvant trastuzumab and chemotherapy-results from the ACOSOG Z1041 (Alliance) trial

Background

HER2 (ERBB2) gene amplification and its corresponding overexpression are present in 15-30% of invasive breast cancers. While HER2-targeted agents are effective treatments, resistance remains a major cause of death. The American College of Surgeons Oncology Group Z1041 trial (NCT00513292) was designed to compare the pathologic complete response (pCR) rate of distinct regimens of neoadjuvant chemotherapy and trastuzumab, but ultimately identified no difference.

Patients and methods

In supplement to tissues from 37 Z1041 cases, 11 similarly treated cases were obtained from a single institution study (NCT00353483). We have extracted genomic DNA from both pre-treatment tumor biopsies and blood of these 48 cases, and performed whole genome (WGS) and exome sequencing. Coincident with these efforts, we have generated RNA-seq profiles from 42 of the tumor biopsies. Among patients in this cohort, 24 (50%) achieved a pCR.

Results

We have characterized the genomic landscape of HER2-positive breast cancer and investigated associations between genomic features and pCR. Cases assigned to the HER2-enriched subtype by RNA-seq analysis were more likely to achieve a pCR compared to the luminal, basal-like, or normal-like subtypes (19/27 versus 3/15; P = 0.0032). Mutational events led to the generation of putatively active neoantigens, but were overall not associated with pCR. ERBB2 and GRB7 were the genes most commonly observed in fusion events, and genomic copy number analysis of the ERBB2 locus indicated that cases with either no observable or low-level ERBB2 amplification were less likely to achieve a pCR (7/8 versus 17/40; P = 0.048). Moreover, among cases that achieved a pCR, tumors consistently expressed immune signatures that may contribute to therapeutic response.

Conclusion

The identification of these features suggests that it may be possible to predict, at the time of diagnosis, those HER2-positive breast cancer patients who will not respond to treatment with chemotherapy and trastuzumab.

ClinicalTrials.gov identifiers

NCT00513292, NCT00353483.

HER kinase inhibition in patients with HER2- and HER3-mutant cancers

Somatic mutations of ERBB2 and ERBB3 (which encode HER2 and HER3, respectively) are found in a wide range of cancers. Preclinical modelling suggests that a subset of these mutations lead to constitutive HER2 activation, but most remain biologically uncharacterized. Here we define the biological and therapeutic importance of known oncogenic HER2 and HER3 mutations and variants of unknown biological importance by conducting a multi-histology, genomically selected, 'basket' trial using the pan-HER kinase inhibitor neratinib (SUMMIT; clinicaltrials.gov identifier NCT01953926). Efficacy in HER2-mutant cancers varied as a function of both tumour type and mutant allele to a degree not predicted by preclinical models, with the greatest activity seen in breast, cervical and biliary cancers and with tumours that contain kinase domain missense mutations. This study demonstrates how a molecularly driven clinical trial can be used to refine our biological understanding of both characterized and new genomic alterations with potential broad applicability for advancing the paradigm of genome-driven oncology.

HER kinase inhibition in patients with HER2- and HER3-mutant cancers

Somatic mutations of ERBB2 and ERBB3 (which encode HER2 and HER3, respectively) are found in a wide range of cancers. Preclinical modelling suggests that a subset of these mutations lead to constitutive HER2 activation, but most remain biologically uncharacterized. Here we define the biological and therapeutic importance of known oncogenic HER2 and HER3 mutations and variants of unknown biological importance by conducting a multi-histology, genomically selected, 'basket' trial using the pan-HER kinase inhibitor neratinib (SUMMIT; clinicaltrials.gov identifier NCT01953926). Efficacy in HER2-mutant cancers varied as a function of both tumour type and mutant allele to a degree not predicted by preclinical models, with the greatest activity seen in breast, cervical and biliary cancers and with tumours that contain kinase domain missense mutations. This study demonstrates how a molecularly driven clinical trial can be used to refine our biological understanding of both characterized and new genomic alterations with potential broad applicability for advancing the paradigm of genome-driven oncology.

Metastatic HER2+ Breast Cancer: A Potentially Curable Disease?

The advent of trastuzumab and other human epidermal growth factor receptor 2 (HER2)-directed therapies has revolutionized the treatment of metastatic HER2-positive breast cancer, leading to prolonged survival and appreciable clinical benefit for a substantial subset of patients. Previously, in a retrospective study at our institution, we observed that nearly 10% of patients achieved a durable complete remission (DCR) following a combination of HER2-directed therapy and cytotoxic chemotherapy. We are currently expanding this study to include patients who were treated since the initial introduction of trastuzumab. From our ongoing study, we present a selected case series of three patients with metastatic HER2-positive breast cancer who achieved a DCR. It is theorized that metastatic HER2-positive breast cancer may be potentially curable in certain patients with favorable clinicopathological and molecular factors, which the patients within our case series mostly demonstrate. These include de novo presentation, estrogen receptor (ER)-negative status, limited disease burden, and absence of deleterious gene or pathway mutations. More research is needed in order to incorporate these findings into clinical practice.

HER2-positive breast cancer is lost in translation: time for patient-centered research

No biomarker beyond HER2 itself, which suffers from a low positive predictive value, has demonstrated clinical utility in breast cancer, despite numerous attempts to improve treatment tailoring for the growing number of anti-HER2 targeted therapies. This prompted us to examine the body of evidence, using a systematic approach, to identify putative predictive biomarkers in HER2-positive breast cancer, and discuss the hitherto failure to address the needs of patients. In the future, it is hoped immune-based biomarkers will predict benefit from anti-HER2 treatments in the neoadjuvant and adjuvant settings. In advanced-stage disease, the quantification of tumour heterogeneity using molecular-imaging technology has generated informative data on the success or failure of the antibody-drug conjugate T-DM1. Treatment tailoring remains a high priority, in cost-constrained health-care systems, but such tailoring will require a dramatic shift in the way translational research is being conducted, with the establishment of large, easily accessible, and well-annotated databases of candidate predictive biomarkers. Single-centre biomarker research should become a thing of the past.

A novel pathway-based distance score enhances assessment of disease heterogeneity in gene expression

BACKGROUND

Distance based unsupervised clustering of gene expression data is commonly used to identify heterogeneity in biologic samples. However, high noise levels in gene expression data and relatively high correlation between genes are often encountered, so traditional distances such as Euclidean distance may not be effective at discriminating the biological differences between samples. An alternative method to examine disease phenotypes is to use pre-defined biological pathways. These pathways have been shown to be perturbed in different ways in different subjects who have similar clinical features. We hypothesize that differences in the expressions of genes in a given pathway are more predictive of differences in biological differences compared to standard approaches and if integrated into clustering analysis will enhance the robustness and accuracy of the clustering method. To examine this hypothesis, we developed a novel computational method to assess the biological differences between samples using gene expression data by assuming that ontologically defined biological pathways in biologically similar samples have similar behavior.

RESULTS

Pre-defined biological pathways were downloaded and genes in each pathway were used to cluster samples using the Gaussian mixture model. The clustering results across different pathways were then summarized to calculate the pathway-based distance score between samples. This method was applied to both simulated and real data sets and compared to the traditional Euclidean distance and another pathway-based clustering method, Pathifier. The results show that the pathway-based distance score performs significantly better than the Euclidean distance, especially when the heterogeneity is low and genes in the same pathways are correlated. Compared to Pathifier, we demonstrated that our approach achieves higher accuracy and robustness for small pathways. When the pathway size is large, by downsampling the pathways into smaller pathways, our approach was able to achieve comparable performance.

CONCLUSIONS

We have developed a novel distance score that represents the biological differences between samples using gene expression data and pre-defined biological pathway information. Application of this distance score results in more accurate, robust, and biologically meaningful clustering results in both simulated data and real data when compared to traditional methods. It also has comparable or better performance compared to Pathifier.

Immune Gene Expression Is Associated with Genomic Aberrations in Breast Cancer

The presence of tumor-infiltrating lymphocytes (TIL) is a favorable prognostic factor in breast cancer, but what drives immune infiltration remains unknown. Here we examine if clonal heterogeneity, total mutation load, neoantigen load, copy number variations (CNV), gene- or pathway-level somatic mutations, or germline polymorphisms (SNP) are associated with immune metagene expression in breast cancer subtypes. Thirteen published immune metagenes correlated separately with genomic metrics in the three major breast cancer subtypes. We analyzed RNA-Seq, DNA copy number, mutation and germline SNP data of 627 ER⁺, 207 HER2⁺, and 191 triple-negative (TNBC) cancers from The Cancer Genome Atlas. P-values were adjusted for multiple comparisons, and permutation testing was used to assess false discovery rates. Increased immune metagene expression associated significantly with lower clonal heterogeneity estimated by MATH score in all subtypes and with a trend for lower overall mutation, neoantigen, and CNV loads in TNBC and HER2⁺ cancers. In ER⁺ cancers, mutation load, neoantigen load, and CNV load weakly but positively associated with immune infiltration, which reached significance for overall mutation load only. No highly recurrent single gene or pathway level mutations associated with immune infiltration. High immune gene expression and lower clonal heterogeneity in TNBC and HER2⁺ cancers suggest an immune pruning effect and equilibrium between immune surveillance and clonal expansion. Thus, immune checkpoint inhibitors may tip the balance in favor of immune surveillance in these cancers. Cancer Res; 77(12); 3317-24. ©2017 AACR.