Biomarkers for HER2-positive metastatic breast cancer: Beyond hormone receptors.

The overexpression of human epidermal growth factor receptor-2 (HER2) results in a biologically and clinically aggressive breast cancer (BC) subtype. Since the introduction of anti-HER2 targeted agents, survival rates of patients with HER2-positive metastatic BC have dramatically improved. Currently, although the treatment decision process in metastatic BC is primarily based on HER2 and hormone-receptor (HR) status, a rapidly growing body of data suggests that several other sources of biological heterogeneity may characterize HER2-positive metastatic BC. Moreover, pivotal clinical trials of new anti-HER2 antibody-drug conjugates showed encouraging results in HER2-low metastatic BC, thus leading to the possibility, in the near future, to expand the pool of patients suitable for HER2-targeted treatments. The present review summarizes and puts in perspective available evidence on biomarkers that hold the greatest promise to become potentially useful tools for optimizing HER2-positive metastatic BC patients' prognostic stratification and treatment in the next future. These biomarkers include HER2 levels and heterogeneity, HER3, intrinsic molecular subtypes by PAM50 analysis, DNA mutations, and immune-related factors. Molecular discordance between primary and metastatic tumors is also discussed

Exceptional and Durable Responses to TDM-1 After Trastuzumab Failure for Breast Cancer Skin Metastases: Potential Implications of an Immunological Sanctuary

Breast Cancer (BC) skin metastases represent a challenging clinical scenario. Although they usually arise when other distant metastases are already present, they may also represent a form of locoregional recurrence (LRR). Systemic therapy in this setting may have a role both in case a radical locoregional approach is unfeasible in order to achieve disease control, and as adjuvant strategy after radical removal of cutaneous lesions, in order to prevent or delay subsequent disease spread. Systemic therapy for HER2+ metastatic BC (MBC) currently relies on anti-HER2 targeted agents. In this context TDM1 is an option in trastuzumab-resistant patients.Here we present 2 cases of isolated skin metastases in patients with HER2+ BC progressing during or early after trastuzumab-based therapy, showing impressive responses to TDM1. We hypothesize that the unique properties of skin immune microenvironment may explain the failure of trastuzumab, which exerts its action also through immunological mechanisms, and the subsequent outlier responses to TDM1, that relies on a partially different mechanism of action

Rare breast cancer subtypes: Histological, molecular, and clinical peculiarities

Breast cancer encompasses a collection of different diseases characterized by different biological and pathological features, clinical presentation, response to treatments, clinical behavior, and outcome. On the basis of cell morphology, growth, and architecture patterns, breast cancer can be classified in up to 21 distinct histological types. Breast cancer special types, including the classic lobular invasive carcinoma, represent 25% of all breast cancers. The histological diversity of breast carcinomas has relevant prognostic implications. Indeed, the rare breast cancer group includes subtypes with very different prognoses, ranging from the tubular carcinoma, associated with an indolent clinical course, to metaplastic cancer, whose outcome is generally unfavorable. New approaches based on gene expression profiling allow the identification of molecularly defined breast cancer classes, with distinct biological features and clinical behavior. In clinical practice, immunohistochemical classification based on the expression of human epidermal growth factor receptor 2 and Ki67 is applied as a surrogate of the intrinsic molecular subtypes. However, the identification of intrinsic molecular subtypes were almost completely limited to the study of ductal invasive breast cancer. Moreover, some good-prognosis triple-negative histotypes, on the basis of gene expression profiling, can be classified among the poor-prognosis group. Therefore, histopathological classification remains a crucial component of breast cancer diagnosis. Special histologies can be very rare, and the majority of information on outcome and treatments derives from small series and case reports. As a consequence, clear recommendations about clinical management are still lacking. In this review, we summarize current knowledge about rare breast cancer histologies.Breast cancer encompasses a collection of different diseases characterized by different biological and pathological features, clinical presentation, response to treatments, clinical behavior, and outcome. On the basis of cell morphology, growth, and architecture patterns, breast cancer can be classified in up to 21 distinct histological types. Breast cancer special types, including the classic lobular invasive carcinoma, represent 25% of all breast cancers. The histological diversity of breast carcinomas has relevant prognostic implications. Indeed, the rare breast cancer group includes subtypes with very different prognoses, ranging from the tubular carcinoma, associated with an indolent clinical course, to metaplastic cancer, whose outcome is generally unfavorable. New approaches based on gene expression profiling allow the identification of molecularly defined breast cancer classes, with distinct biological features and clinical behavior. In clinical practice, immunohistochemical classification based on the expression of human epidermal growth factor receptor 2 and Ki67 is applied as a surrogate of the intrinsic molecular subtypes. However, the identification of intrinsic molecular subtypes were almost completely limited to the study of ductal invasive breast cancer. Moreover, some good-prognosis triple-negative histotypes, on the basis of gene expression profiling, can be classified among the poor-prognosis group. Therefore, histopathological classification remains a crucial component of breast cancer diagnosis. Special histologies can be very rare, and the majority of information on outcome and treatments derives from small series and case reports. As a consequence, clear recommendations about clinical management are still lacking. In this review, we summarize current knowledge about rare breast cancer histologies. \ua9 AlphaMed Press

Blurring of boundaries in the doctor-patient relationship

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Le Cellule Staminali Mesenchimali: Aspetti Biologici ed Approcci Terapeutici

The pioneeristic studies, started thirty years ago, were able to uncover an interesting bone marrow derived cell population named as mesenchymal stem cells (MSC). This cell type, used in the last decade in both pre-clinical and clinical phases in several fields of biomedical sciences, opened up innovative branches of translational research. In this review we analyze the biological background of the MSC with the purpose to identify their actual therapeutical applications with a special focus on their possible future role in oncology

Real world data in the era of Immune Checkpoint Inhibitors (ICIs): Increasing evidence and future applications in lung cancer.

Immune checkpoint inhibitors (ICIs) targeting programmed death 1 (PD-1) and PD-ligand 1 (PD-L1) quickly subverted the standard of treatment in Non-Small Cell Lung Cancer (NSCLC), where they were first introduced in all comers previously treated advanced/metastatic NSCLC patients and subsequently in the first line of PD-L1 selected cases of metastatic and locally advanced disease. Treatment algorithm is an evolving landscape, where the introduction of front-line ICIs, with or without chemotherapy, unavoidably influences the following treatment lines. In this context, medical oncologists are currently facing many unclear issues, which have been not clarified so far by available data. Effectiveness and safety in special populations underrepresented in clinical trials - such as elderly, poor PS, hepatitis or human immunodeficiency virus-affected patients - are only a part of the unexplored side of ICIs in the real world. Indeed, pivotal randomized clinical trials (RCTs) often lack of external validity because eligibility criteria exclude some patient subgroups commonly treated in real-world clinical practice. Similarly, cost-effectiveness and sustainability of these innovative agents are important issues to be considered in the real-world. Though affected by several limitations, real-world evidence (RWE) studies allow to collect data regarding overall treated patients in clinical practice according to local authority regulations, overcoming the intrinsic limits of RCTs. The present review focuses on RWE about ICIs in lung cancer treatment, with particular reference to special patient populations, and discusses potential application of real-world data in a potential innovative drug development model

The Probabilistic Random Forest applied to the selection of quasar candidates in the QUBRICS Survey

The number of known, bright ($i2.5$) QSOs in the Southern Hemisphere is considerably lower than the corresponding number in the Northern Hemisphere due to the lack of multi-wavelength surveys at $\delta<0$. Recent works, such as the QUBRICS survey, successfully identified new, high-redshift QSOs in the South by means of a machine learning approach applied on a large photometric dataset. Building on the success of QUBRICS, we present a new QSO selection method based on the Probabilistic Random Forest (PRF), an improvement of the classic Random Forest algorithm. The PRF takes into account measurement errors, treating input data as probability distribution functions: this allows us to obtain better accuracy and a robust predictive model. We applied the PRF to the same photometric dataset used in QUBRICS, based on the SkyMapper DR1, Gaia DR2, 2MASS, WISE and GALEX databases. The resulting candidate list includes $626$ sources with $i<18$. We estimate for our proposed algorithm a completeness of $\sim84\%$ and a purity of $\sim78\%$ on the test datasets. Preliminary spectroscopic campaigns allowed us to observe 41 candidates, of which 29 turned out to be $z>2.5$ QSOs. The performances of the PRF, currently comparable to those of the CCA, are expected to improve as the number of high-z QSOs available for the training sample grows: results are however already promising, despite this being one of the first applications of this method to an astrophysical context.Comment: Accepted for publication in MNRAS, 12 pages, 11 figures, 4 table