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

A multispectral 3D live organoid imaging platform to screen probes for fluorescence guided surgery

Achieving complete tumor resection is challenging and can be improved by real-time fluorescence-guided surgery with molecular-targeted probes. However, pre-clinical identification and validation of probes presents a lengthy process that is traditionally performed in animal models and further hampered by inter- and intra-tumoral heterogeneity in target expression. To screen multiple probes at patient scale, we developed a multispectral real-time 3D imaging platform that implements organoid technology to effectively model patient tumor heterogeneity and, importantly, healthy human tissue binding.

iGenSig-Rx: an integral genomic signature based white-box tool for modeling cancer therapeutic responses using multi-omics data

Multi-omics sequencing is poised to revolutionize clinical care in the coming decade. However, there is a lack of effective and interpretable genome-wide modeling methods for the rational selection of patients for personalized interventions. To address this, we present iGenSig-Rx, an integral genomic signature-based approach, as a transparent tool for modeling therapeutic response using clinical trial datasets. This method adeptly addresses challenges related to cross-dataset modeling by capitalizing on high-dimensional redundant genomic features, analogous to reinforcing building pillars with redundant steel rods. Moreover, it integrates adaptive penalization of feature redundancy on a per-sample basis to prevent score flattening and mitigate overfitting. We then developed a purpose-built R package to implement this method for modeling clinical trial datasets. When applied to genomic datasets for HER2 targeted therapies, iGenSig-Rx model demonstrates consistent and reliable predictive power across four independent clinical trials. More importantly, the iGenSig-Rx model offers the level of transparency much needed for clinical application, allowing for clear explanations as to how the predictions are produced, how the features contribute to the prediction, and what are the key underlying pathways. We anticipate that iGenSig-Rx, as an interpretable class of multi-omics modeling methods, will find broad applications in big-data based precision oncology. The R package is available: https://github.com/wangxlab/iGenSig-Rx .

The implications of single-cell RNA-seq analysis in prostate cancer: unraveling tumor heterogeneity, therapeutic implications and pathways towards personalized therapy

In recent years, advancements in single-cell and spatial transcriptomics, which are highly regarded developments in the current era, particularly the emerging integration of single-cell and spatiotemporal transcriptomics, have enabled a detailed molecular comprehension of the complex regulation of cell fate. The insights obtained from these methodologies are anticipated to significantly contribute to the development of personalized medicine. Currently, single-cell technology is less frequently utilized for prostate cancer compared with other types of tumors. Starting from the perspective of RNA sequencing technology, this review outlined the significance of single-cell RNA sequencing (scRNA-seq) in prostate cancer research, encompassing preclinical medicine and clinical applications. We summarize the differences between mouse and human prostate cancer as revealed by scRNA-seq studies, as well as a combination of multi-omics methods involving scRNA-seq to highlight the key molecular targets for the diagnosis, treatment, and drug resistance characteristics of prostate cancer. These studies are expected to provide novel insights for the development of immunotherapy and other innovative treatment strategies for castration-resistant prostate cancer. Furthermore, we explore the potential clinical applications stemming from other single-cell technologies in this review, paving the way for future research in precision medicine.

Caveolin-1 gene expression provides additional prognostic information combined with PAM50 risk of recurrence (ROR) score in breast cancer

Combining information from the tumor microenvironment (TME) with PAM50 Risk of Recurrence (ROR) score could improve breast cancer prognostication. Caveolin-1 (CAV1) is a marker of an active TME. CAV1 is a membrane protein involved in cell signaling, extracellular matrix organization, and tumor-stroma interactions. We sought to investigate CAV1 gene expression in relation to PAM50 subtypes, ROR score, and their joint prognostic impact. CAV1 expression was compared between PAM50 subtypes and ROR categories in two cohorts (SCAN-B, n = 5326 and METABRIC, n = 1980). CAV1 expression was assessed in relation to clinical outcomes using Cox regression and adjusted for clinicopathological predictors. Effect modifications between CAV1 expression and ROR categories on clinical outcome were investigated using multiplicative and additive two-way interaction analyses. Differential gene expression and gene set enrichment analyses were applied to compare high and low expressing CAV1 tumors. All samples expressed CAV1 with the highest expression in the Normal-like subtype. Gene modules consistent with epithelial-mesenchymal transition (EMT), hypoxia, and stromal activation were associated with high CAV1 expression. CAV1 expression was inversely associated with ROR category. Interactions between CAV1 expression and ROR categories were observed in both cohorts. High expressing CAV1 tumors conferred worse prognosis only within the group classified as ROR high. ROR gave markedly different prognostic information depending on the underlying CAV1 expression. CAV1, a potential mediator between the malignant cells and TME, could be a useful biomarker that enhances and further refines PAM50 ROR risk stratification in patients with ROR high tumors and a potential therapeutic target.

Predictive Markers of Treatment Response to Neoadjuvant Systemic Therapy with Dual HER2-Blockade

In patients with human epidermal growth factor receptor 2 (HER2)-positive breast cancer, achievement of pathologic complete response (pCR) is a known prognostic indicator after neoadjuvant systemic therapy (NAST). We investigated the clinicopathological factors associated with pCR in patients with HER2-positive breast cancer treated with dual HER2-blockade. In this retrospective study, 348 patients with HER2-positive breast cancer who received NAST with docetaxel and carboplatin, combined with trastuzumab and pertuzumab (TCHP), were included. Of the 348 patients with HER2 protein expression data, 278 (79.9%) had HER2 immunochemistry (IHC) 3+. Data on tumor-infiltrating lymphocyte (TIL) levels were available for 305 patients, showing a median TIL level of 20% (IQR 5-50), among which 121 (39.7%) had high TIL levels (≥30%). Estrogen receptor (ER) status (77.9% in ER-negative vs. 47.5% in ER-positive; p < 0.001), HER2 protein expression (71.6% in IHC 3+ vs. 34.3% in IHC 2+; p < 0.001), and TIL levels (71.9% in high vs. 57.6% in low; p = 0.011) were significantly associated with the pCR rate. In addition, we observed a significant link between numerical TIL levels (per 10% increment) and the pCR rate. After adjusting other clinicopathologic factors, ER status (low expression [defined as 1-9% expression] or negative), HER2 IHC 3+ and numerical TIL levels (per 10% increment), and high TIL levels (≥30%) were found to be independent predictors of pCR. Notably, in ER-negative breast cancer, the treatment response was excellent, irrespective of HER2 expression and TIL levels. Conversely, in ER-positive cases, low ER expression, HER2 IHC 3+, and numerical TIL levels or high TIL levels emerged as independent predictors of pCR. Our results suggest that ER expression, HER2 protein expression, and TIL levels serve as valuable predictors of the treatment response to neoadjuvant TCHP.

Real-world data on neoadjuvant chemotherapy with dual-anti HER2 therapy in HER2 positive breast cancer

BACKGROUND

Neoadjuvant chemotherapy with dual-targeted therapy is the standard treatment for human epidermal growth factor 2 (HER2)-positive breast cancer. Although the dual-targeted therapy has significantly improved the pathological complete response (pCR) rate, further investigation is needed to identify biomarkers that predict the response to neoadjuvant therapy.

METHODS

This retrospective study analyzed 353 patients with HER2-positive breast invasive ductal carcinoma. The correlation between clinicopathological factors and pCR rate was evaluated. A nomogram was constructed based on the results of the multivariate logistic regression analysis to predict the probability of pCR.

RESULTS

The breast pCR (b-pCR) rate was 56.1% (198/353) and the total pCR (t-pCR) rate was 52.7% (186/353). Multivariate analysis identified ER status, PR status, HER2 status, Ki-67 index, and neoadjuvant chemotherapy regimens as independent indicators for both b-pCR and t-pCR. The nomogram had an area under the receiver operating characteristic curve (AUC) of 0.73 (95% CI: 0.68-0.78). According to the nomogram, the t- pCR rate was highest in the ER-PR- HER2-positive patients (131/208) and lowest in the ER + PR + HER2-positive patients (19/73). The subgroup analyses showed that there was no significant difference in pCR rate among the neoadjuvant chemotherapy regimens in ER positive, PR positive, HER2 IHC 2 + , Ki67 index < 30% population. However, for ER-PR-HER2-positive patients, the neoadjuvant chemotherapy regimen has a great influence on the pCR rates.

CONCLUSIONS

Patients with ER-negative, PR-negative, HER2 3 + and high KI-67 index were more likely to achieve pCR. THP may be used as an alternative to AC-THP or TCbHP in selected HER2-positive patients.

Identification of genes and pathways associated with menopausal status in breast cancer patients using two algorithms

BACKGROUND

Menopausal status has a known relationship with the levels of estrogen, progesterone, and other sex hormones, potentially influencing the activity of ER, PR, and many other signaling pathways involved in the initiation and progression of breast cancer. However, the differences between premenopausal and postmenopausal breast cancer patients at the molecular level are unclear.

METHODS

We retrieved eight datasets from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) associated with menopausal status in breast cancer patients were identified using the MAMA and LIMMA methods. Based on these validated DEGs, we performed Gene Ontology (GO) functional enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses. Protein-protein interaction (PPI) networks were constructed. We used DrugBank data to investigate which of these validated DEGs are targetable. Survival analysis was performed to explore the influence of these genes on breast cancer patient prognosis.

RESULTS

We identified 762 DEGs associated with menopausal status in breast cancer patients. PPI network analysis indicated that these genes are primarily involved in pathways such as the cell cycle, oocyte meiosis and progesterone-mediated oocyte maturation pathways. Notably, several genes played roles in multiple signaling pathways and were associated with patient survival. These genes were also observed to be targetable according to the DrugBank database.

CONCLUSION

We identified DEGs associated with menopausal status in breast cancer patients. The association of these genes with several key pathways may promote understanding of the complex characterizations of breast cancer. Our findings offer valuable insights for developing new therapeutic strategies tailored to the menopausal status of breast cancer patients.

The inhibitory effect of trastuzumab on BT474 triple‑positive breast cancer cell viability is reversed by the combination of progesterone and estradiol

Breast cancer expressing the estrogen receptor (ER), progesterone receptor (PR) and human epidermal growth factor receptor-2 (HER2) is known as triple-positive (TPBC). TPBC represents 9-11% of breast cancer cases worldwide and is a heterogeneous subtype. Notably, TPBC presents a therapeutic challenge due to the crosstalk between the hormonal (ER and PR) and HER2 pathways. Patients with TPBC are treated with trastuzumab (TTZ); however, several patients treated with TTZ tend to relapse. The present study aimed to investigate the effect of the PR on inhibitory effect of TTZ on cell viability. BT474 cells (a model of TPBC) and BT474 PR-silenced cells were treated with either TTZ, progesterone (Pg), the PR antagonist mifepristone (RU486) or estradiol (E2) alone or in combination for 144 h (6 days). Cell viability assays and western blotting were subsequently performed. The results showed that Pg and E2 interfered with the inhibitory effect of TTZ on cell viability and this effect was potentiated when both hormones were combined. Pg was revealed to act through the PR, mainly activating the PR isoform B (PR-B) and inducing the protein expression levels of CDK4 and cyclin D1; however, it did not reactivate the HER2/Akt pathway. By contrast, E2 was able to increase PR isoform A (PR-A) expression, which was inhibited by Pg. Notably, in most of the experiments, RU486 did not antagonize the effects of Pg. In conclusion, Pg and E2 may interfere with the inhibitory effect of TTZ on cell viability through PR-B activation and PR-A inactivation.

iGenSig-Rx: an integral genomic signature based white-box tool for modeling cancer therapeutic responses using multi-omics data

Multi-omics sequencing is expected to become clinically routine within the next decade and transform clinical care. However, there is a paucity of viable and interpretable genome-wide modeling methods that can facilitate rational selection of patients for tailored intervention. Here we develop an integral genomic signature-based method called iGenSig-Rx as a white-box tool for modeling therapeutic response based on clinical trial datasets with improved cross-dataset applicability and tolerance to sequencing bias. This method leverages high-dimensional redundant genomic features to address the challenges of cross-dataset modeling, a concept similar to the use of redundant steel rods to reinforce the pillars of a building. Using genomic datasets for HER2 targeted therapies, the iGenSig-Rx model demonstrates stable predictive power across four independent clinical trials. More importantly, the iGenSig-Rx model offers the level of transparency much needed for clinical application, allowing for clear explanations as to how the predictions are produced, how the features contribute to the prediction, and what are the key underlying pathways. We expect that iGenSig-Rx as a class of biologically interpretable multi-omics modeling methods will have broad applications in big-data based precision oncology. The R package is available: https://github.com/wangxlab/iGenSig-Rx. NOTE: the Github website will be released upon publication and the R package is available for review through google drive: https://drive.google.com/drive/folders/1KgecmUoon9-h2Dg1rPCyEGFPOp28Ols3?usp=sharing.

Differential response of HER2-positive breast cancer to anti-HER2 therapy based on HER2 protein expression level

BACKGROUND

Increasing data indicate that HER2-positive (HER2 + ) breast cancer (BC) subtypes exhibit differential responses to targeted anti-HER2 therapy. This study aims to investigate these differences and the potential underlying molecular mechanisms.

METHODS

A large cohort of BC patients (n = 7390) was utilised. The clinicopathological characteristics and differential gene expression (DGE) of HER2+ immunohistochemical (IHC) subtypes, specifically HER2 IHC 3+ and IHC 2 + /Amplified, were assessed and correlated with pathological complete response (pCR) and survival in the neoadjuvant and adjuvant settings, respectively. The role of oestrogen receptor (ER) status was also investigated.

RESULTS

Compared to HER2 IHC 3+ tumours, BC patients with IHC 2 + /Amplified showed a significantly lower pCR rate (22% versus 57%, P < 0.001), shorter survival regardless of HER2 gene copy number, were less classified as HER2 enriched, and enriched for trastuzumab resistance and ER signalling pathway genes. ER positivity significantly decreased response to anti-HER2 therapy in IHC 2 + /Amplified, but not in IHC 3 + BC patients.

CONCLUSION

In HER2 + BC, overexpression of HER2 protein is the driver of the oncogenic pathway, and it is the main predictor of response to anti-HER2 therapy. ER signalling pathways are more dominant in BC with equivocal HER2 expression. personalised anti-HER2 therapy based on IHC classes should be considered.

A Review of Endocrine Therapy in Early-stage Breast Cancer: The Journey From Crudeness to Precision

Endocrine therapy (ET) is the standard of care for hormone receptor-positive early-stage breast cancer in the adjuvant setting. However, response to ET can vary across patient subgroups. Historically, hormone receptor expression and clinical stage are the main predictors of the benefit of ET. A "window of opportunity" trials has raised significant interest in recent years as a means of assessing the sensitivity of a patient's cancer to short-term neoadjuvant ET, which provides important prognostic information, and helps in decision-making regarding treatment options in a time-efficient and cost-efficient manner. In the era of genomics, molecular profiling has led to the discovery and evaluation of the prognostic and predictive abilities of new molecular profiles. To realize the goal of personalized medicine, we are in urgent need to explore reliable biomarkers or genomic signatures to accurately predict the clinical response and long-term outcomes associated with ET. Validation of these biomarkers as reliable surrogate endpoints can also lead to a revolution in the clinical trial designs, and potentially avoid the need for repeated tissue biopsies in the surveillance of disease response. The clinical potential of tumor genomic profiling marks the beginning of a new era of precision medicine in breast cancer treatment.

A generalized non-linear model predicting efficacy of neoadjuvant therapy in HER2+ breast cancer

Neoadjuvant therapy (NAT) is currently recommended to patients with human epidermal growth factor receptor 2-positive breast cancer (HER2+ BC) that typically exhibit a poor prognosis. The tumor immune microenvironment profoundly affects the efficacy of NAT. However, the correlation between tumor-infiltrating lymphocytes or their specific subpopulations and the response to NAT in HER2+ BC remains largely unknown. In our study, the immune infiltration status of 295 patients was classified as "immune-rich" or "immune-poor" phenotypes. The "immune-rich" phenotype was significantly positively related to pathological complete response (pCR). Ten genes were correlated with both pCR and the immune phenotype based on the results of spline and logistic regression. We constructed a generalized non-linear model combining linear and non-linear gene effects and successfully validated its predictive power using an internal and external validation set (AUC = 0.819, 0.797; respectively) and a clinical set (accuracy = 0.75).

Review of the status of neoadjuvant therapy in HER2-positive breast cancer

Purpose

The development of human epidermal growth factor receptor 2 (HER2)-directed therapies has revolutionized the treatment of HER2-positive breast cancer. The aim of this article is to review the continually evolving treatment strategies in the neoadjuvant setting of HER2-positive breast cancer, as well as the current challenges and future perspectives.

Methods

Searches were undertaken on PubMed and Clinicaltrials.gov for relevant publications and trials.

Findings

The current standard of care in high-risk HER2-positive breast cancer is to combine chemotherapy with dual anti-HER2 therapy, for a synergistic anti-tumor effect. We discuss the pivotal trials which led to the adoption of this approach, as well as the benefit of these neoadjuvant strategies for guiding appropriate adjuvant therapy. De-escalation strategies are currently being investigated to avoid over treatment, and aim to safely reduce chemotherapy, while optimizing HER2-targeted therapies. The development and validation of a reliable biomarker is essential to enable these de-escalation strategies and personalization of treatment. In addition, promising novel therapies are currently being explored to further improve outcomes in HER2-positive breast cancer.

CTTN Overexpression Confers Cancer Stem Cell-like Properties and Trastuzumab Resistance via DKK-1/WNT Signaling in HER2 Positive Breast Cancer

BACKGROUND

Despite the therapeutic success of trastuzumab, HER2 positive (HER2+) breast cancer patients continue to face significant difficulties due to innate or acquired drug resistance. In this study we explored the potential role of CTTN in inducing trastuzumab resistance of HER2+ breast cancers.

METHODS

Genetic changes of CTTN and survival of HER2+ breast cancer patients were analyzed in multiple breast cancer patient cohorts (METABRIC, TCGA, Kaplan-Meier (KM) plotter, and Hanyang University cohort). The effect of CTTN on cancer stem cell activity was assessed using the tumorsphere formation, ALDEFLUOR assay, and by in vivo xenograft experiments. CTTN-induced trastuzumab resistance was assessed by the sulforhodamine B (SRB) assay, colony formation assays, and in vivo xenograft model. RNA-seq analysis was used to clarify the mechanism of trastuzumab resistance conferred by CTTN.

RESULTS

Survival analysis indicated that CTTN overexpression is related to a poor prognosis in HER2+ breast cancers (OS, p = 0.05 in the Hanyang University cohort; OS, p = 0.0014 in KM plotter; OS, p = 0.008 and DFS, p = 0.010 in METABRIC). CTTN overexpression-induced cancer stem cell-like characteristics in experiments of tumorsphere formation, ALDEFLUOR assays, and in vivo limiting dilution assays. CTTN overexpression resulted in trastuzumab resistance in SRB, colony formation assays, and in vivo xenograft models. Mechanistically, the mRNA and protein levels of DKK-1, a Wnt antagonist, were downregulated by CTTN. Treatment of the β-catenin/TCF inhibitor reversed CTTN-induced cancer stem cell-like properties in vitro. Combination treatment with trastuzumab and β-catenin/TCF inhibitor overcame trastuzumab resistance conferred by CTTN overexpression in in vitro colony formation assays.

CONCLUSIONS

CTTN activates DKK-1/Wnt/β-catenin signaling to induce trastuzumab resistance. We propose that CTTN is a novel biomarker indicating a poor prognosis and a possible therapeutic target for overcoming trastuzumab resistance.

Clinically oriented prediction of patient response to targeted and immunotherapies from the tumor transcriptome

BACKGROUND

Precision oncology is gradually advancing into mainstream clinical practice, demonstrating significant survival benefits. However, eligibility and response rates remain limited in many cases, calling for better predictive biomarkers.

METHODS

We present ENLIGHT, a transcriptomics-based computational approach that identifies clinically relevant genetic interactions and uses them to predict a patient's response to a variety of therapies in multiple cancer types without training on previous treatment response data. We study ENLIGHT in two translationally oriented scenarios: personalized oncology (PO), aimed at prioritizing treatments for a single patient, and clinical trial design (CTD), selecting the most likely responders in a patient cohort.

FINDINGS

Evaluating ENLIGHT's performance on 21 blinded clinical trial datasets in the PO setting, we show that it can effectively predict a patient's treatment response across multiple therapies and cancer types. Its prediction accuracy is better than previously published transcriptomics-based signatures and is comparable with that of supervised predictors developed for specific indications and drugs. In combination with the interferon-γ signature, ENLIGHT achieves an odds ratio larger than 4 in predicting response to immune checkpoint therapy. In the CTD scenario, ENLIGHT can potentially enhance clinical trial success for immunotherapies and other monoclonal antibodies by excluding non-responders while overall achieving more than 90% of the response rate attainable under an optimal exclusion strategy.

CONCLUSIONS

ENLIGHT demonstrably enhances the ability to predict therapeutic response across multiple cancer types from the bulk tumor transcriptome.

FUNDING

This research was supported in part by the Intramural Research Program, NIH and by the Israeli Innovation Authority.

Tumor biomarkers and efficacy in patients treated with trastuzumab emtansine + pertuzumab versus standard of care in HER2-positive early breast cancer: an open-label, phase III study (KRISTINE)

BACKGROUND

KRISTINE is an open-label, phase III study of trastuzumab emtansine + pertuzumab (T-DM1 + P) versus docetaxel + carboplatin + trastuzumab + pertuzumab (TCH + P) in patients with HER2-positive, stage II-III breast cancer. We investigated the association of biomarkers with clinical outcomes in KRISTINE.

METHODS

Patients were randomized to receive neoadjuvant T-DM1 + P or TCH + P and assessed for pathologic complete response (pCR; ypT0/is, ypN0). HER2 status (per central assessment), hormone receptor status, PIK3CA mutation status, HER2/HER3 mRNA levels, tumor-infiltrating lymphocyte levels, PD-L1 status, and NanoString data were analyzed. pCR rates by treatment arm were compared across biomarker subgroups. Analyses were descriptive.

RESULTS

Biomarker analyses included data from all 444 patients (T-DM1 + P, n = 223; TCH + P, n = 221) enrolled in KRISTINE. Biomarker distribution was balanced across treatment arms. All subgroups with higher HER2 amplification/expression and immune marker levels showed numerically higher pCR rates in both arms. Mutated versus non-mutated PIK3CA tumors were associated with numerically lower pCR rates in the T-DM1 + P arm but not in the TCH + P arm. In a multivariate analysis, Prediction Analysis of Microarray with the 50-gene classifier (PAM50) HER2-enriched subtype, HER2 gene ratio ≥ 4, and PD-L1-positive status positively influenced the pCR rate. Biomarkers associated with lower pCR rates (e.g., low HER2 levels, positive hormone receptor status, mutated PIK3CA) were more likely to co-occur. Dynamic on-treatment biomarker changes were observed. Differences in the treatment effects for T-DM1 + P versus TCH + P were similar to those observed in the intent-to-treat population for the majority of the biomarker subgroups.

CONCLUSIONS

Although our biomarker analysis did not identify a subgroup of patients that benefited from neoadjuvant T-DM1 + P versus TCH + P, the data revealed that patients with higher HER2 amplification/expression and immune marker levels had improved response irrespective of treatment arm. These analyses confirm the role of HER2 tumor biology and the immune microenvironment in influencing pCR in the neoadjuvant setting and reaffirm the molecular diversity of HER2-positive breast cancer.

TRIAL REGISTRATION

ClinicalTrials.gov NCT02131064. Registered 06 May 2014.

A phase II study of palbociclib plus letrozole plus trastuzumab as neoadjuvant treatment for clinical stages II and III ER+ HER2+ breast cancer (PALTAN)

Patients with ER+/HER2+ breast cancer (BC) are less likely to achieve pathological complete response (pCR) after chemotherapy with dual HER2 blockade than ER-/HER2+ BC. Endocrine therapy plus trastuzumab is effective in advanced ER+/HER2+ BC. Inhibition of CDK4/6 and HER2 results in synergistic cell proliferation reduction. We combined palbociclib, letrozole, and trastuzumab (PLT) as a chemotherapy-sparing regimen. We evaluated neoadjuvant PLT in early ER+/HER2+ BC. Primary endpoint was pCR after 16 weeks. Research biopsies were performed for whole exome and RNA sequencing, PAM50 subtyping, and Ki67 assessment for complete cell cycle arrest (CCCA: Ki67 ≤ 2.7%). After 26 patients, accrual stopped due to futility. pCR (residual cancer burden-RCB 0) was 7.7%, RCB 0/I was 38.5%. Grade (G) 3/4 treatment-emergent adverse events occurred in 19. Among these, G3/4 neutropenia was 50%, hypertension 26.9%, and leucopenia 7.7%. Analysis indicated CCCA in 85% at C1 day 15 (C1D15), compared to 27% at surgery after palbociclib was discontinued. Baseline PAM50 subtyping identified 31.2% HER2-E, 43.8% Luminal B, and 25% Luminal A. 161 genes were differentially expressed comparing C1D15 to baseline. MKI67, TK1, CCNB1, AURKB, and PLK1 were among the genes downregulated, consistent with CCCA at C1D15. Molecular Signatures Database gene-sets analyses demonstrated downregulated processes involved in proliferation, ER and mTORC1 signaling, and DNA damage repair at C1D15, consistent with the study drug's mechanisms of action. Neoadjuvant PLT showed a pCR of 7.7% and an RCB 0/I rate of 38.5%. RNA sequencing and Ki67 data indicated potent anti-proliferative effects of study treatments. ClinicalTrials.gov- NCT02907918.

Three-year disease-free survival in randomized trials of neoadjuvant chemotherapy and HER2-targeted therapy in breast cancer: A meta-analysis

BACKGROUND

Outcomes for breast cancer patients with residual disease (RD) after neoadjuvant chemotherapy (NACT) and HER2-targeted therapy may be better than anticipated leading to a smaller absolute benefit of adjuvant trastuzumab emtansine (T-DM1). Therefore, accurate estimates of 3-year disease-free survival (DFS) can aid in treatment planning.

METHODS

We reviewed randomized trials of NACT and HER2-targeted therapy in breast cancer (excluding T-DM1) and calculated mean 3-year DFS weighted by study sample size. Meta-regression comprising linear regression weighted by sample size (mixed-effects) was performed to explore associations between 3-year DFS and year of accrual and trial-level patient, disease, and treatment factors. Data were reported quantitatively irrespective of statistical significance.

RESULTS

Eleven studies (N = 3581) were included in the primary analysis. The mean 3-year DFS for patients with RD was 79.7% (95% CI 77.4-80.9). This was higher for trials completing accrual after 2010 [83% (95% CI 79.3-86.3)] and for those receiving dual HER2 targeted therapy [83.4% (95% CI 79.2-87.7]. Better outcomes for ER positivity, later accrual and dual Her-2 targeted therapy were confirmed in meta-regression. Negative quantitative significance was observed for larger clinical tumor size and nodal involvement.

CONCLUSIONS

The 3-year DFS for patients with RD has improved over time possibly due to dual HER2 targeted therapy. This will reduce the absolute benefit of adjuvant T-DM1 in this group of patients.

Chromosomal Instability as Enabling Feature and Central Hallmark of Breast Cancer

Chromosomal instability (CIN) has become a topic of great interest in recent years, not only for its implications in cancer diagnosis and prognosis but also for its role as an enabling feature and central hallmark of cancer. CIN describes cell-to-cell variation in the number or structure of chromosomes in a tumor population. Although extensive research in recent decades has identified some associations between CIN with response to therapy, specific associations with other hallmarks of cancer have not been fully evidenced. Such associations place CIN as an enabling feature of the other hallmarks of cancer and highlight the importance of deepening its knowledge to improve the outcome in cancer. In addition, studies conducted to date have shown paradoxical findings about the implications of CIN for therapeutic response, with some studies showing associations between high CIN and better therapeutic response, and others showing the opposite: associations between high CIN and therapeutic resistance. This evidences the complex relationships between CIN with the prognosis and response to treatment in cancer. Considering the above, this review focuses on recent studies on the role of CIN in cancer, the cellular mechanisms leading to CIN, its relationship with other hallmarks of cancer, and the emerging therapeutic approaches that are being developed to target such instability, with a primary focus on breast cancer. Further understanding of the complexity of CIN and its association with other hallmarks of cancer could provide a better understanding of the cellular and molecular mechanisms involved in prognosis and response to treatment in cancer and potentially lead to new drug targets.

Gene Expression Profiles in Cancers and Their Therapeutic Implications

ABSTRACT

The vast amount of gene expression profiling data of bulk tumors and cell lines available in the public domain represents a tremendous resource. For any major cancer type, expression data can identify molecular subtypes, predict patient outcome, identify markers of therapeutic response, determine the functional consequences of somatic mutation, and elucidate the biology of metastatic and advanced cancers. This review provides a broad overview of gene expression profiling in cancer (which may include transcriptome and proteome levels) and the types of findings made using these data. This review also provides specific examples of accessing public cancer gene expression data sets and generating unique views of the data and the resulting genes of interest. These examples involve pan-cancer molecular subtyping, metabolism-associated expression correlates of patient survival involving multiple cancer types, and gene expression correlates of chemotherapy response in breast tumors.

Clinical implications of the intrinsic molecular subtypes in hormone receptor-positive and HER2-negative metastatic breast cancer

Traditionally, the classification of breast cancer relies on the expression of immunohistochemical (IHC) biomarkers readily available in clinical practice. Using highly standardized and reproducible assays across patient cohorts, intrinsic molecular subtypes of breast cancer - also called "intrinsic subtypes" (IS) - have been identified based on the expression of 50 genes. Although IHC-based subgroups and IS moderately correlate to each other, they are not superimposable. In fact, non-luminal biology has been detected in a substantial proportion (5-20%) of hormone receptor-positive (HoR+) tumors, has prognostic value, and identifies reduced and increased sensitivity to endocrine therapy and chemotherapy, respectively. During tumor progression, a shift toward a non-luminal estrogen-independent and more aggressive phenotype has been demonstrated. Intrinsic genomic instability and cell plasticity, alone or combined with external constraints deriving from treatment selective pressure or interplay with the tumor microenvironment, may represent the determinants of such biological diversity between primary and metastatic disease, and during metastatic tumor evolution. In this review, we describe the distribution and the clinical behavior of IS as the disease progresses, focusing on HoR+/HER2-negative advanced breast cancer. In addition, we provide an overview of the ongoing clinical trials aiming to validate the predictive and prognostic value of IS towards their incorporation into routine care.

Predictive Role of CD36 Expression in HER2-Positive Breast Cancer Patients Receiving Neoadjuvant Trastuzumab

BACKGROUND

Despite huge efforts to identify biomarkers associated with long-term clinical outcomes in patients with early-stage HER2-positive breast cancer (HER2+ BC) treated with (neo)adjuvant anti-HER2 therapy, no reliable predictors have been identified so far. Fatty acid uptake, a process mediated by the transmembrane transporter CD36, has recently emerged as a potential determinant of resistance to anti-HER2 treatments in preclinical HER2+ BC models.

METHODS

Here, we investigated the association between baseline intratumor CD36 gene expression and event-free survival in 180 patients enrolled in the phase III trial Neoadjuvant Lapatinib and/or Trastuzumab Treatment Optimization (NeoALTTO), which randomly assigned stage II-III HER2+ BC patients to receive neoadjuvant lapatinib, trastuzumab, or lapatinib-trastuzumab in combination with chemotherapy. To this aim, we selected NeoALTTO trial patients for whom pretreatment whole transcriptomic data were available. The main study results were validated in an independent cohort of patients enrolled in the neoadjuvant phase II trial NeoSphere.

RESULTS

In 180 NeoALTTO patients, high intratumor CD36 expression was independently associated with worse event-free survival in patients treated with trastuzumab-based therapy (hazard ratio [HR] = 1.72, 95% confidence interval [CI] = 1.20 to 2.46), but not with lapatinib-based (HR = 1.02, 95% CI = 0.68 to 1.53) or trastuzumab-lapatinib-based (HR = 1.08, 95% CI = 0.60 to 1.94) therapy. Among 331 NeoSphere patients evaluated, high CD36 expression was independently associated with worse patient disease-free survival in both the whole study cohort (HR = 1.197, 95% CI = 1.002 to 1.428) and patients receiving trastuzumab-based neoadjuvant therapy (HR = 1.282, 95% CI = 1.049 to 1.568).

CONCLUSIONS

High CD36 expression predicts worse clinical outcomes in early-stage HER2+ BC treated with trastuzumab-based neoadjuvant therapy.

Targeted immunotherapy against distinct cancer-associated fibroblasts overcomes treatment resistance in refractory HER2+ breast tumors

About 50% of human epidermal growth factor receptor 2 (HER2)+ breast cancer patients do not benefit from HER2-targeted therapy and almost 20% of them relapse after treatment. Here, we conduct a detailed analysis of two independent cohorts of HER2+ breast cancer patients treated with trastuzumab to elucidate the mechanisms of resistance to anti-HER2 monoclonal antibodies. In addition, we develop a fully humanized immunocompetent model of HER2+ breast cancer recapitulating ex vivo the biological processes that associate with patients’ response to treatment. Thanks to these two approaches, we uncover a population of TGF-beta-activated cancer-associated fibroblasts (CAF) specific from tumors resistant to therapy. The presence of this cellular subset related to previously described myofibroblastic (CAF-S1) and podoplanin+ CAF subtypes in breast cancer associates with low IL2 activity. Correspondingly, we find that stroma-targeted stimulation of IL2 pathway in unresponsive tumors restores trastuzumab anti-cancer efficiency. Overall, our study underscores the therapeutic potential of exploiting the tumor microenvironment to identify and overcome mechanisms of resistance to anti-cancer treatment.

A substantial proportion of HER2+ breast cancer patients do not benefit from HER2-targeted therapy. Here, the authors identify a population of cancer-associated fibroblasts involved in the suppression of trastuzumab-induced ADCC that can be pharmacologically targeted to raise treatment effectiveness in unresponsive tumors.

Does the 21-gene recurrence score have clinical utility in HR+/HER2+ breast cancer?

The 21-gene recurrence score assay has been validated as a predictive biomarker in early-stage HR+ and HER2-breast cancer. It is not indicated for use in HER2+ disease based on national guidelines. In this study, we assessed the value of 21-gene recurrence score (RS), or OncotypeDX (ODX), testing in HR+/HER2+ breast cancer. We used the National Cancer Database to identify patients with stages I-II, HR+/HER2+ breast cancer who received multi-gene testing with ODX. We then explored the prognostic and predictive value of this biomarker through various forms of survival modeling. ODX testing was performed in n = 5,280 patients. N = 2,678 patients (50.7%) had a RS < 26, while n = 2,602 (49.3%) had a RS ≥26. In Kaplan-Meier survival modeling for patients with recurrence scores <26, there was no significant difference in overall survival (p = 0.445) between patients receiving different systemic treatment regimens. However, when recurrence scores were ≥26, there was a statistically-significant difference in overall survival between systemic treatment regimens (p < 0.001). 5-year overall survival was highest (97.4%) for patients receiving triple therapy (anti-HER2 with chemotherapy and endocrine therapy), followed by those receiving dual therapy with endocrine and anti-HER2 (96.7%), and endocrine with chemotherapy (94.9%). Patients receiving endocrine therapy alone exhibited the lowest 5-year overall survival (88.5%). RESULTS: Analysis from this large national cancer registry suggests that multigene testing may have predictive value in treatment selection for patients with early-stage, HR+/HER2+ breast cancer. Prospective trials are warranted to identify subgroups of patients with HR+/HER2+ breast cancer who can be spared anti-HER2 treatments and cytotoxic chemotherapy.

Dynamic changes in intrinsic subtype, immunity status, and risk score before and after neoadjuvant chemo- and HER2-targeted therapy without pCR in HER2-positive breast cancers: A cross-sectional analysis

Very few studies have been done in HER2 positive patients without complete pathological response (pCR) after combined neoadjuvant chemo- and HER2-target therapy to investigate changes in intrinsic subtype, risk of recurrence (ROR) score, and immunity status before and after treatment. Patients with nonmetastatic HER2-positive breast cancer failed to achieve pCR after neoadjuvant chemotherapy plus trastuzumab were included in current study. We examined the distribution of PAM50 subtypes, ROR score and immunity score in 25 paired baseline and surgical samples. The Miller-Payne grading system was used to evaluate the efficacy of the neoadjuvant therapy. It was observed that the distribution of intrinsic subtype, ROR category and immunity subgroup varied according to hormone receptor (HR) status. HER2-enriched and basal-like subtypes, median-high ROR categories and immunity-weak subgroup were dominant in baseline tumors. Compared to baseline samples, conversion of intrinsic subtype, ROR categories and immunity subgroups were found in 15 (60.0%), 13(52.0%), and 11(44.0%) surgical samples, respectively. The PAM50 subtype, ROR category, and immunity subgroup were concordant between baseline and surgical samples where nonluminal subtypes, median-high ROR categories and i-weak subgroup were still common. In conclusion, the HER2-positive breast cancer is highly heterogeneous with a distribution of 72-gene expression varying according to HR co-expression. The dynamics of the 72-gene expression pre- and posttreatment may become novel biomarker for guiding adjuvant therapy and hence warrant further investigation.

Targeted Therapeutic Options and Future Perspectives for HER2-Positive Breast Cancer

Simple Summary

The development of several antiHuman Epidermal Growth Factor Receptor 2 (HER2) treatments over the last few years has improved the landscape of HER2-positive breast cancer. Despite this, relapse is still the main issue in HER2-positive breast cancer. The reasons for therapeutic failure lie in the heterogeneity of the disease itself, as well as in the drug resistance mechanisms. In this review, we intended to understand the milestones that have had an impact on this disease up to their implementation in clinical practice. In addition, understanding the underlying molecular biology of HER2-positive disease is essential for the optimization and personalization of the different treatment options. For this reason, we focused on two relevant aspects, which are triple-positive disease and the role that modulation of the immune response might play in treatment and prognosis.

Abstract

Despite the improvement achieved by the introduction of HER2-targeted therapy, up to 25% of early human epidermal growth factor receptor 2-positive (HER2+) breast cancer (BC) patients will relapse. Beyond trastuzumab, other agents approved for early HER2+ BC include the monoclonal antibody pertuzumab, the antibody-drug conjugate (ADC) trastuzumab-emtansine (T-DM1) and the reversible HER2 inhibitor lapatinib. New agents, such as trastuzumab-deruxtecan or tucatinib in combination with capecitabine and trastuzumab, have also shown a significant improvement in the metastatic setting. Other therapeutic strategies to overcome treatment resistance have been explored in HER2+ BC, mainly in HER2+ that also overexpress estrogen receptors (ER+). In ER+ HER2+ patients, target therapies such as phosphoinositide-3-kinase (PI3K) pathway inhibition or cyclin-dependent kinases 4/6 blocking may be effective in controlling downstream of HER2 and many of the cellular pathways associated with resistance to HER2-targeted therapies. Multiple trials have explored these strategies with some promising results, and probably, in the next years conclusive results will succeed. In addition, HER2+ BC is known to be more immunogenic than other BC subgroups, with high variability between tumors. Different immunotherapeutic agents such as HER-2 therapy plus checkpoint inhibitors, or new vaccines approaches have been investigated in this setting, with promising but controversial results obtained to date.

Fatty acid synthase: A druggable driver of breast cancer brain metastasis

INTRODUCTION

Brain metastasis (BrM) is a key contributor to morbidity and mortality in breast cancer patients, especially among high-risk epidermal growth factor receptor 2-positive (HER2+) and triple-negative/basal-like molecular subtypes. Optimal management of BrM is focused on characterizing a "BrM dependency map" to prioritize targetable therapeutic vulnerabilities.

AREAS COVERED

We review recent studies addressing the targeting of BrM in the lipid-deprived brain environment, which selects for brain-tropic breast cancer cells capable of cell-autonomously generating fatty acids by upregulating de novo lipogenesis via fatty acid synthase (FASN). Disruption of FASN activity impairs breast cancer growth in the brain, but not extracranially, and mapping of the molecular causes of organ-specific patterns of metastasis has uncovered an enrichment of lipid metabolism signatures in brain metastasizing cells. Targeting SREBP1-the master regulator of lipogenic gene transcription-curtails the ability of breast cancer cells to survive in the brain microenvironment.

EXPERT OPINION

Targeting FASN represents a new therapeutic opportunity for patients with breast cancer and BrM. Delivery of brain-permeable FASN inhibitors and identifying strategies to target metabolic plasticity that might compensate for impaired brain FASN activity are two potential roadblocks that may hinder FASN-centered strategies against BrM.

Decision Theory versus Conventional Statistics for Personalized Therapy of Breast Cancer

Estrogen and progesterone receptors being present or not represents one of the most important biomarkers for therapy selection in breast cancer patients. Conventional measurement by immunohistochemistry (IHC) involves errors, and numerous attempts have been made to increase precision by additional information from gene expression. This raises the question of how to fuse information, in particular, if there is disagreement. It is the primary domain of Dempster–Shafer decision theory (DST) to deal with contradicting evidence on the same item (here: receptor status), obtained through different techniques. DST is widely used in technical settings, such as self-driving cars and aviation, and is also promising to deliver significant advantages in medicine. Using data from breast cancer patients already presented in previous work, we focus on comparing DST with classical statistics in this work, to pave the way for its application in medicine. First, we explain how DST not only considers probabilities (a single number per sample), but also incorporates uncertainty in a concept of ‘evidence’ (two numbers per sample). This allows for very powerful displays of patient data in so-called ternary plots, a novel and crucial advantage for medical interpretation. Results are obtained according to conventional statistics (ODDS) and, in parallel, according to DST. Agreement and differences are evaluated, and the particular merits of DST discussed. The presented application demonstrates how decision theory introduces new levels of confidence in diagnoses derived from medical data.

High RAD51 gene expression is associated with aggressive biology and with poor survival in breast cancer

PURPOSE

Although the DNA repair mechanism is important in preventing carcinogenesis, its activation in established cancer cells may support their proliferation and aggravate cancer progression. RAD51 cooperates with BRCA2 and is essential in the homologous recombination of DNA repair. To this end, we hypothesized that RAD51 gene expression is associated with cancer cell proliferation and poor prognosis of breast cancer (BC) patients.

METHODS

A total of 8515 primary BC patients with transcriptome and clinical data from 17 independent cohorts were analyzed. The median value was used to divide each cohort into high and low RAD51 expression groups.

RESULTS

High RAD51 expression enriched the DNA repair gene set and was correlated with DNA repair-related genes. Nottingham histological grade, Ki67 expression and cell proliferation-related gene sets (E2F Targets, G2M Checkpoint and Myc Targets) were all significantly associated with the high RAD51 BC group. RAD51 expression was positively correlated with Homologous Recombination Deficiency, as well as both mutational burden and neoantigens that accompanied a higher infiltration of immune cells. Primary BC with lymph node metastases was associated with high expression of RAD51 in two cohorts. There was no strong correlation between RAD51 expression and drug sensitivity in cell lines, and RAD51 expression was lower after the neoadjuvant chemotherapy compared to before the treatment. High RAD51 BC was associated with poor prognosis consistently in three independent cohorts.

CONCLUSION

RAD51 gene expression is associated with aggressive cancer biology, cancer cell proliferation, and poor survival in breast cancer.

Circulating Proteins Associated with Response and Resistance to Neoadjuvant Chemotherapy in HER2-Positive Breast Cancer

Simple Summary

The goal of this study was to find circulating proteins that can be easily sampled and incorporated into a clinical setting to improve predictive treatment response in HER2-positive breast cancer patients receiving neoadjuvant chemotherapy. We looked for potential biomarkers in serum, which we identified using two proteomics techniques: qualitative LC-MS/MS and a quantitative assay that assessed protein expression between responders and non-responders HER2-positive breast cancer patients to neoadjuvant chemotherapy.

Abstract

Despite the increasing use of neoadjuvant chemotherapy (NAC) in HER2-positive breast cancer (BC) patients, the clinical problem of predicting individual treatment response remains unanswered. Furthermore, the use of ineffective chemotherapeutic regimens should be avoided. Serum biomarker levels are being studied more and more for their ability to predict therapy response and aid in the development of personalized treatment regimens. This study aims to identify effective protein networks and biomarkers to predict response to NAC in HER2-positive BC patients through an exhaustive large-scale LC-MS/MS-based qualitative and quantitative proteomic profiling of serum samples from responders and non-responders. Serum samples from HER2-positive BC patients were collected before NAC and were processed by three methods (with and without nanoparticles). The qualitative analysis revealed differences in the proteomic profiles between responders and non-responders, mainly in proteins implicated in the complement and coagulation cascades and apolipoproteins. Qualitative analysis confirmed that three proteins (AFM, SERPINA1, APOD) were correlated with NAC resistance. In this study, we show that serum biomarker profiles can predict treatment response and outcome in the neoadjuvant setting. If these findings are further developed, they will be of significant clinical utility in the design of treatment regimens for individual BC patients.

Trilaciclib Prior to Chemotherapy in Patients with Metastatic Triple-Negative Breast Cancer: Final Efficacy and Subgroup Analysis from a Randomized Phase II Study

PURPOSE

We report final antitumor efficacy results from a phase II study of trilaciclib, an intravenous cyclin-dependent kinase 4/6 (CDK4/6) inhibitor, administered prior to gemcitabine plus carboplatin (GCb) in patients with metastatic triple-negative breast cancer (NCT02978716).

PATIENTS AND METHODS

Patients were randomized (1:1:1) to group 1 [GCb (days 1, 8); n = 34], group 2 [trilaciclib prior to GCb (days 1, 8); n = 33], or group 3 [trilaciclib (days 1, 8) and trilaciclib prior to GCb (days 2, 9); n = 35]. Subgroup analyses were performed according to CDK4/6 dependence, level of programmed death-ligand 1 (PD-L1) expression, and RNA-based immune signatures using proportional hazards regression. T-cell receptor (TCR) β CDR3 regions were amplified and sequenced to identify, quantify, and compare the abundance of each unique TCRβ CDR3 at baseline and on treatment.

RESULTS

Median overall survival (OS) was 12.6 months in group 1, not reached in group 2 (HR = 0.31; P = 0.0016), 17.8 months in group 3 (HR = 0.40; P = 0.0004), and 19.8 months in groups 2 and 3 combined (HR = 0.37; P < 0.0001). Efficacy outcomes were comparable regardless of cancer CDK4/6 dependence status and immune signatures. Administering trilaciclib prior to GCb prolonged OS irrespective of PD-L1 status but had greater benefit in the PD-L1-positive population. T-cell activation was enhanced in patients receiving trilaciclib.

CONCLUSIONS

Administering trilaciclib prior to GCb enhanced antitumor efficacy, with significant improvements in OS. Efficacy outcomes in immunologic subgroups and enhancements in T-cell activation suggest these improvements may be mediated via immunologic mechanisms.

HER2+ Breast Cancer Escalation and De-Escalation Trial Design: Potential Role of Intrinsic Subtyping

Long-term outcomes in breast cancer patients differ based on the molecular subtype, with HER2-E being the most aggressive one. Advances in clinical practice have dramatically shifted HER2+ breast cancer prognosis. Risk adapted strategies to individualize therapies are necessary. De-escalation approaches have been encouraged based on the risks of clinical-pathological factors. Molecular gene subtyping could further accurately define HER2 addicted tumours that are sensitive to anti-HER2 therapies, thus sparing unnecessary treatments. The transition from immunochemistry to molecular profiling in HER2+ breast cancer is discussed.

Comparing Biomarkers for Predicting Pathological Responses to Neoadjuvant Therapy in HER2-Positive Breast Cancer: A Systematic Review and Meta-Analysis

INTRODUCTION

The predictive strength and accuracy of some biomarkers for the pathological complete response (pCR) to neoadjuvant therapy for HER2-positive breast cancer remain unclear. This study aimed to compare the accuracy of the HER2-enriched subtype and the presence of PIK3CA mutations, namely, TILs, HRs, and Ki-67, in predicting the pCR to HER2-positive breast cancer therapy.

METHODS

We screened studies that included pCR predicted by one of the following biomarkers: the HER2-enriched subtype and the presence of PIK3CA mutations, TILs, HRs, or Ki-67. We then calculated the pooled sensitivity, specificity, positive and negative predictive values (PPVs and NPVs, respectively), and positive and negative likelihood ratios (LRs). Summary receiver operating characteristic (SROC) curves and areas under the curve (AUCs) were used to estimate the diagnostic accuracy.

RESULTS

The pooled estimates of sensitivity and specificity for the HER2-enriched subtype and the presence of PIK3CA mutations, namely, TILs, HRs, and Ki-67, were 0.66 and 0.62, 0.85 and 0.27, 0.49 and 0.61, 0.54 and 0.64, and 0.68 and 0.51, respectively. The AUC of the HER2-enriched subtype was significantly higher (0.71) than those for the presence of TILs (0.59, p = 0.003), HRs (0.65, p = 0.003), and Ki-67 (0.62, p = 0.005). The AUC of the HER2-enriched subtype had a tendency to be higher than that of the presence of PIK3CA mutations (0.58, p = 0.220). Moreover, it had relatively high PPV (0.58) and LR+ (1.77), similar NPV (0.73), and low LR- (0.54) compared with the other four biomarkers.

CONCLUSIONS

The HER2-enriched subtype has a moderate breast cancer diagnostic accuracy, which is better than those of the presence of PIK3CA mutations, TILs, HRs, and Ki-67.

De novo Metastatic Breast Cancer Arising in Young Women: Review of the Current Evidence

Women with metastatic breast cancer remains a heterogeneous group of patients with different prognostic outcomes and therapeutic needs. Young women with de novo metastatic breast cancer (dnMBC) represent a peculiar population with respect to tumor biology, prognosis, clinical management and survivorship issues. Overall, these patients are able to attain long-term survival with a proper management of both primary tumor and distant metastases. On the other hand, they are also at higher risk of experiencing a deterioration in their quality of life (QoL) due to primary cancer-related side effects. Young women are also likely to harbor germline pathogenic variants in cancer predisposition genes which could affect treatment decisions and have a direct impact on the lives of patients' relatives. The loco-regional management of the primary tumor represents another thorny subject, as the surgical approach has shown controversial effects on the survival and the QoL of these patients. This review aims to provide an update on these issues to better inform the clinical management of dnMBC in young women.

Impact of molecular subtype and race on HR+, HER2- breast cancer survival

PURPOSE

There is an urgent need to understand the biological factors contributing to the racial survival disparity among women with hormone receptor-positive (HR+), HER2- breast cancer. In this study, we examined the impact of PAM50 subtype on 10-year mortality rate in women with HR+, HER2- breast cancer by race.

METHODS

Women with localized, HR+, HER2- breast cancer diagnosed between 2002 and 2012 from two population-based cohorts were evaluated. Archival tumors were obtained and classified by PAM50 into four molecular subtypes (i.e., luminal A, luminal B, HER2-enriched, and basal-like). The molecular subtypes within HR+, HER2- breast cancers and corresponding 10-year mortality rate were compared between Black and Non-Hispanic White (NHW) women using Cox proportional hazard ratios and survival analysis, adjusting for covariates.

RESULTS

In this study, 318 women with localized, HR+, HER2- breast cancer were included-227 Black (71%) and 91 NHW (29%). Young Black women (age ≤ 50) had the highest proportion of HR+, non-luminal A tumors (47%), compared to young NHW (10%), older Black women (31%), and older NHW (30%). Overall, women with HR+, non-luminal A subtypes had a higher 10-year mortality rate compared to HR+, luminal A subtypes after adjustment for age, stage, and income (HR 4.21 for Blacks, 95% CI 1.74-10.18 and HR 3.44 for NHW, 95% CI 1.31-9.03). Among HR+, non-luminal A subtypes there was, however, no significant racial difference in 10-yr mortality observed (Black vs. NHW: HR 1.23, 95% CI 0.58-2.58).

CONCLUSION

Molecular subtype classification highlights racial disparities in PAM50 subtype distribution among women with HR+, HER2- breast cancer. Among women with HR+, HER2- breast cancer, racial survival disparities are ameliorated after adjusting for molecular subtype.

Immune Markers and Tumor-Related Processes Predict Neoadjuvant Therapy Response in the WSG-ADAPT HER2-Positive/Hormone Receptor-Positive Trial in Early Breast Cancer

Prognostic or predictive biomarkers in HER2-positive early breast cancer (EBC) may inform treatment optimization. The ADAPT HER2-positive/hormone receptor-positive phase II trial (NCT01779206) demonstrated pathological complete response (pCR) rates of ~40% following de-escalated treatment with 12 weeks neoadjuvant ado-trastuzumab emtansine (T-DM1) ± endocrine therapy. In this exploratory analysis, we evaluated potential early predictors of response to neoadjuvant therapy. The effects of PIK3CA mutations and immune (CD8 and PD-L1) and apoptotic markers (BCL2 and MCL1) on pCR rates were assessed, along with intrinsic BC subtypes. Immune response and pCR were lower in PIK3CA-mutated tumors compared with wildtype. Increased BCL2 at baseline in all patients and at Cycle 2 in the T-DM1 arms was associated with lower pCR. In the T-DM1 arms only, the HER2-enriched subtype was associated with increased pCR rate (54% vs. 28%). These findings support further prospective pCR-driven de-escalation studies in patients with HER2-positive EBC.

A systematic review of datasets that can help elucidate relationships among gene expression, race, and immunohistochemistry-defined subtypes in breast cancer

Scholarly requirements have led to a massive increase of transcriptomic data in the public domain, with millions of samples available for secondary research. We identified gene-expression datasets representing 10,214 breast-cancer patients in public databases. We focused on datasets that included patient metadata on race and/or immunohistochemistry (IHC) profiling of the ER, PR, and HER-2 proteins. This review provides a summary of these datasets and describes findings from 32 research articles associated with the datasets. These studies have helped to elucidate relationships between IHC, race, and/or treatment options, as well as relationships between IHC status and the breast-cancer intrinsic subtypes. We have also identified broad themes across the analysis methodologies used in these studies, including breast cancer subtyping, deriving predictive biomarkers, identifying differentially expressed genes, and optimizing data processing. Finally, we discuss limitations of prior work and recommend future directions for reusing these datasets in secondary analyses.

Dissecting the biological heterogeneity of HER2-positive breast cancer

HER2-positive (HER2+) breast cancer (BC) is a heterogenous and multifaceted disease, with interesting therapeutic implications. First, all intrinsic molecular subtypes can be identified in HER2+ tumors, with the HER2-enriched being the most frequent. Such subtypes do not differ much from their counterparts in HER2-negative disease, apart for the high expression of genes in/near the HER2 amplicon on chromosome 17. Intrinsic subtyping, along with the quantification of ERBB2 mRNA levels, is associated with higher rates of pathologic complete response across neoadjuvant trials of dual HER2 blockade and might help select patients for de-escalation and escalation treatment strategies. Secondly, HER2+ tumors have a broad range of DNA alterations. ERBB2 mutations and alterations in the PI3K/Akt/mTOR pathway are among the most frequent and might predict benefit from potent pan-HER, PI3K and mTOR inhibitors. Moreover, HER2+ tumors are usually infiltrated by lymphocytes. These tumor infiltrating-lymphocytes (TILs) predict response to neoadjuvant anti-HER2-based treatment and exert a prognostic role. PD-L1, detected in ∼42 % of HER2+ BC, might also be useful to define patients responding to novel anti-PD1/PD-L1 immunotherapies. New multiparametric clinicopathologic and genomic tools accounting for this complexity, such as HER2DX, are under development to define more tailored treatment approaches. Finally, HER2-targeted antibody-drug conjugates (ADC) such as trastuzumab deruxtecan might be active in tumors with low expression of HER2. Overall, there is a need to molecularly characterize and develop novel targeted therapies for HER2+ disease.

•

Almost 50 % of HER2+ breast cancer (BC) are molecularly HER2-Enriched (HER2-E).

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Most relevant mutations are found in ERBB2 (∼4 %) and PI3K/AKT/mTOR pathway (>30 %).

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Tumor infiltrating lymphocytes are frequent, predictive and prognostic in HER2+ BC.

•

HER2 heterogeneity and HER2 low status are gaining therapeutic relevance.

•

New treatments need to consider HER2+ molecular and microenvironmental complexity.

Meta-Analysis of HER2-Enriched Subtype Predicting the Pathological Complete Response Within HER2-Positive Breast Cancer in Patients Who Received Neoadjuvant Treatment

Background

This meta-analysis aimed to better elucidate the predictive value of human epidermal growth factor receptor 2 (HER2)-enriched subtype of pathological complete response (pCR) rate within HER2-positive breast cancer patients receiving neoadjuvant treatment.

Methods

We identified prospective trials that evaluated the correlation between an HER2-enriched subtype and pCR rate in HER2-positive breast cancer. Pooled odds ratio (OR) values with 95% confidence intervals (CIs) were computed.

Results

Fifteen studies comprising 2,190 patients met the inclusion criteria. The HER2-enriched subtype was associated with increased odds of achieving a pCR (OR = 4.12, 95% CI = 3.38 to 5.03, P < 0.001) in patients overall. Moreover, it was correlated with improved pCR when single or dual HER2-targeted agent-based therapy was employed (OR = 3.36, 95% CI = 2.25 to 5.02, P < 0.001; OR = 4.66, 95% CI = 3.56 to 6.10, P < 0.001, respectively), but not when HER2-targeted agent-free chemotherapy was used (OR = 2.52, 95% CI = 0.98 to 6.49, P = 0.05). Moreover, an HER2-enriched subtype predicted higher pCR rates irrespective of HER2-targeted agents (trastuzumab, lapatinib, pertuzumab, or T-DM1); chemotherapy agents (taxane-based, or anthracyclines plus taxane-based); endocrine therapy and hormone receptor [all the differences were statistically significant (P all ≤ 0.001)].

Conclusions

The HER2-enriched subtype can more effectively and specifically predict pCR for HER2-targeted agent-based neoadjuvant treatment, irrespective of the number (single or dual) or category of HER2-targeted agent, including chemotherapy and endocrine therapy, or hormone receptor in cases of HER2-positive breast cancer.

Clinical and Genetic Predictive Models for the Prediction of Pathological Complete Response to Optimize the Effectiveness for Trastuzumab Based Chemotherapy

Background

Trastuzumab shows excellent benefits for HER2+ breast cancer patients, although 20% treated remain unresponsive. We conducted a retrospective cohort study to optimize neoadjuvant chemotherapy and trastuzumab treatment in HER2+ breast cancer patients.

Methods

Six hundred patients were analyzed to identify clinical characteristics of those not achieving a pathological complete response (pCR) to develop a clinical predictive model. Available RNA sequence data was also reviewed to develop a genetic model for pCR.

Results

The pCR rate was 39.8% and pCR was associated with superior disease free survival and overall survival. ER negativity and PR negativity, higher HER2 IHC scores, higher Ki-67, and trastuzumab use were associated with improved pCR. Weekly paclitaxel and carboplatin had the highest pCR rate (46.70%) and the anthracycline+taxanes regimen had the lowest rate (11.11%). Four published GEO datasets were analyzed and a 10-gene model and immune signature for pCR were developed. Non-pCR patients were ER⁺PR⁺ and had a lower immune signature and gene model score. Hormone receptor status and immune signatures were independent predictive factors of pCR.

Conclusion

Hormone receptor status and a 10-gene model could predict pCR independently and may be applied for patient selection and drug effectiveness optimization.

Evaluation of tumor immune contexture among intrinsic molecular subtypes helps to predict outcome in early breast cancer

Background

The prognosis of early breast cancer is linked to clinic-pathological stage and the molecular characteristics of intrinsic tumor cells. In some patients, the amount and quality of tumor-infiltrating immune cells appear to affect long term outcome. We aimed to propose a new tool to estimate immune infiltrate, and link these factors to patient prognosis according to breast cancer molecular subtypes.

Methods

We performed in silico analyses in more than 2800 early breast cancer transcriptomes with corresponding clinical annotations. We first developed a new gene expression deconvolution algorithm that accurately estimates the quantity of immune cell populations (tumor immune contexture, TIC) in tumors. Then, we studied associations between these immune profiles and relapse-free and overall survival among the different intrinsic molecular subtypes of breast cancer defined by PAM50 classification.

Results

TIC estimates the abundance of 15 immune cell subsets. Both myeloid and lymphoid subpopulations show different spread among intrinsic molecular breast cancer subtypes. A high abundance of myeloid cells was associated with poor outcome, while lymphoid cells were associated with favorable prognosis. Unsupervised clustering describing the 15 immune cell subsets revealed four subgroups of breast tumors associated with distinct patient survival, but independent from PAM50. Adding this information to clinical stage and PAM50 strongly improves the prediction of relapse or death.

Conclusions

Our findings make it possible to refine the survival stratification of early patients with breast cancer by incorporating TIC in addition to PAM50 and clinical tumor burden in a prognostic model validated in training and validation cohorts.

Retrospective observational study of HER2 immunohistochemistry in borderline breast cancer patients undergoing neoadjuvant therapy, with an emphasis on Group 2 (HER2/CEP17 ratio ≥2.0, HER2 copy number <4.0 signals/cell) cases

BACKGROUND

The ASCO/CAP guidance on HER2 testing in breast cancer (BC) has recently changed. Group 2 tumours with immunohistochemistry score 2+ and HER2/CEP17 ratio ≥2.0 and HER2 copy number <4.0 signals/cell were re-classified as HER2 negative. This study aims to examine the response of Group 2 tumours to neoadjuvant chemotherapy (NACT).

METHODS

749 BC cases were identified from 11 institutions. The association between HER2 groups and pathological complete response (pCR) was assessed.

RESULTS

54% of immunohistochemistry HER2 positive (score 3+) BCs showed pCR, compared to 19% of immunohistochemistry 2+ FISH amplified cases. 27% of Group 2 treated with HER2 targeted therapy achieved pCR, compared to 19 and 11% in the combined Groups 1 + 3 and Groups 4 + 5, respectively. No difference in pCR rates was identified between Group 2 and Group 1 or combined Groups 1 + 3. However, Group 2 response rate was higher than Groups 4 + 5 (p = 0.017).

CONCLUSION

No difference in pCR was detected in tumours with a HER2/CEP17 ratio ≥2.0 and a HER2 score 2+ by IHC when stratified by HER2 gene copy number. Our data suggest that ASCO/CAP HER2 Group 2 carcinomas should be evaluated further with respect to eligibility for HER2 targeted therapy.

Genetic markers for characterization and prediction of prognosis of melanoma subtypes: a 2021 update

In this article we examined the most important genetic markers involved in melanoma susceptibility, initiation and progression, and their impact on the prognosis of the disease. Current knowledge in melanoma genetics identifies distinct pathways to the development of different melanoma subtypes characterized by specific clinico-pathological features and partially known genetic markers, modulated by high, low or absence of cumulative sun damage. The most prevalent somatic mutations are related to the activation of the MAPK pathway, which are classified into four major subtypes: BRAF mutant, NRAS mutant, NF1 mutant and triple wild type. Moreover, germinal mutations are also involved in the characterization and predictions of prognosis in melanoma. Currently, CDKN2A is seen as the main high-risk gene involved in melanoma susceptibility being mutated in around 20% of melanoma-prone families. Other high-risk susceptibility genes described include CDK4, POT1, BAP1, TERT promoter, ACD, and TERF2IP. Melanoma is one of the most genetically predisposed among all cancers in humans, and ultraviolet light from the sun is the main environmental factor. This genetic predisposition is starting to be understood, impacting not only on the risk of developing melanoma but also on the risk of developing other types of cancer, as well as on the prognosis of the disease.

Evaluating the transcriptional fidelity of cancer models

BACKGROUND

Cancer researchers use cell lines, patient-derived xenografts, engineered mice, and tumoroids as models to investigate tumor biology and to identify therapies. The generalizability and power of a model derive from the fidelity with which it represents the tumor type under investigation; however, the extent to which this is true is often unclear. The preponderance of models and the ability to readily generate new ones has created a demand for tools that can measure the extent and ways in which cancer models resemble or diverge from native tumors.

METHODS

We developed a machine learning-based computational tool, CancerCellNet, that measures the similarity of cancer models to 22 naturally occurring tumor types and 36 subtypes, in a platform and species agnostic manner. We applied this tool to 657 cancer cell lines, 415 patient-derived xenografts, 26 distinct genetically engineered mouse models, and 131 tumoroids. We validated CancerCellNet by application to independent data, and we tested several predictions with immunofluorescence.

RESULTS

We have documented the cancer models with the greatest transcriptional fidelity to natural tumors, we have identified cancers underserved by adequate models, and we have found models with annotations that do not match their classification. By comparing models across modalities, we report that, on average, genetically engineered mice and tumoroids have higher transcriptional fidelity than patient-derived xenografts and cell lines in four out of five tumor types. However, several patient-derived xenografts and tumoroids have classification scores that are on par with native tumors, highlighting both their potential as faithful model classes and their heterogeneity.

CONCLUSIONS

CancerCellNet enables the rapid assessment of transcriptional fidelity of tumor models. We have made CancerCellNet available as a freely downloadable R package ( https://github.com/pcahan1/cancerCellNet ) and as a web application ( http://www.cahanlab.org/resources/cancerCellNet_web ) that can be applied to new cancer models that allows for direct comparison to the cancer models evaluated here.

Spatial proteomic characterization of HER2-positive breast tumors through neoadjuvant therapy predicts response

The addition of HER2-targeted agents to neoadjuvant chemotherapy has dramatically improved pathological complete response (pCR) rates in early-stage, HER2-positive breast cancer. Nonetheless, up to 50% of patients have residual disease after treatment, while others are likely overtreated. Here, we performed multiplex spatial proteomic characterization of 122 samples from 57 HER2-positive breast tumors from the neoadjuvant TRIO-US B07 clinical trial sampled pre-treatment, after 14-21 d of HER2-targeted therapy and at surgery. We demonstrated that proteomic changes after a single cycle of HER2-targeted therapy aids the identification of tumors that ultimately undergo pCR, outperforming pre-treatment measures or transcriptomic changes. We further developed and validated a classifier that robustly predicted pCR using a single marker, CD45, measured on treatment, and showed that CD45-positive cell counts measured via conventional immunohistochemistry perform comparably. These results demonstrate robust biomarkers that can be used to enable the stratification of sensitive tumors early during neoadjuvant HER2-targeted therapy, with implications for tailoring subsequent therapy.

Decision theory for precision therapy of breast cancer

Correctly estimating the hormone receptor status for estrogen (ER) and progesterone (PGR) is crucial for precision therapy of breast cancer. It is known that conventional diagnostics (immunohistochemistry, IHC) yields a significant rate of wrongly diagnosed receptor status. Here we demonstrate how Dempster Shafer decision Theory (DST) enhances diagnostic precision by adding information from gene expression. We downloaded data of 3753 breast cancer patients from Gene Expression Omnibus. Information from IHC and gene expression was fused according to DST, and the clinical criterion for receptor positivity was re-modelled along DST. Receptor status predicted according to DST was compared with conventional assessment via IHC and gene-expression, and deviations were flagged as questionable. The survival of questionable cases turned out significantly worse (Kaplan Meier p < 1%) than for patients with receptor status confirmed by DST, indicating a substantial enhancement of diagnostic precision via DST. This study is not only relevant for precision medicine but also paves the way for introducing decision theory into OMICS data science.

A combination of extracellular matrix- and interferon-associated signatures identifies high-grade breast cancers with poor prognosis

Breast cancer (BC) is a heterogeneous disease in which the tumor microenvironment (TME) seems to impact the clinical outcome. Here, we investigated whether a combination of gene expression signatures relating to both the structural and immune TME aspects can help predict prognosis in women with high‐grade BC (HGBC). Thus, we focused on a combined molecular biomarker variable that involved extracellular matrix (ECM)‐associated gene expression (ECM3 signature) and interferon (IFN)‐associated metagene (IFN metagene) expression. In 97 chemo‐naive HGBCs from the METABRIC dataset, the dichotomous ECM3/IFN (dECIF) variable identified a group of high‐risk patients (ECM3⁺/IFN⁻ vs other; hazard ratio = 3.2, 95% confidence interval: 1.5–6.7). Notably, ECM3⁺/IFN⁻ tumors showed low tumor‐infiltrating lymphocytes, high levels of CD33‐positive cells, absence of PD‐1 expression, or low expression of PD‐L1, as suggested by immune profiles and immune‐histochemical analysis on an independent cohort of 131 HGBCs. To make our results transferable to clinical use, we refined the dECIF biomarker using reduced ECM3 and IFN signatures; notably, the prognostic value of this reduced dECIF was comparable to that of the original dECIF. After validation in a new BC cohort, reduced dECIF was translated into a robust qPCR classifier for real‐world clinical use.

FGFR4 regulates tumor subtype differentiation in luminal breast cancer and metastatic disease

Mechanisms driving tumor progression from less aggressive subtypes to more aggressive states represent key targets for therapy. We identified a subset of luminal A primary breast tumors that give rise to HER2-enriched (HER2E) subtype metastases, but remain clinically HER2 negative (cHER2-). By testing the unique genetic and transcriptomic features of these cases, we developed the hypothesis that FGFR4 likely participates in this subtype switching. To evaluate this, we developed 2 FGFR4 genomic signatures using a patient-derived xenograft (PDX) model treated with an FGFR4 inhibitor, which inhibited PDX growth in vivo. Bulk tumor gene expression analysis and single-cell RNA sequencing demonstrated that the inhibition of FGFR4 signaling caused molecular switching. In the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) breast cancer cohort, FGFR4-induced and FGFR4-repressed signatures each predicted overall survival. Additionally, the FGFR4-induced signature was an independent prognostic factor beyond subtype and stage. Supervised analysis of 77 primary tumors with paired metastases revealed that the FGFR4-induced signature was significantly higher in luminal/ER+ tumor metastases compared with their primaries. Finally, multivariate analysis demonstrated that the FGFR4-induced signature also predicted site-specific metastasis for lung, liver, and brain, but not for bone or lymph nodes. These data identify a link between FGFR4-regulated genes and metastasis, suggesting treatment options for FGFR4-positive patients, whose high expression is not caused by mutation or amplification.