Literature DB >> 19914537

Facilitating consensus by examining patterns of treatment effects.

Richard D Gelber1, Shari Gelber.   

Abstract

Randomized clinical trials are necessary to provide reliable evidence concerning the effectiveness and safety of adjuvant therapies for breast cancer. Such trials, however, are not sufficient to provide information needed to tailor therapies to individual patients. Trials focus on testing treatments on average for heterogeneous patient populations, while attention to the specific characteristics of the disease and the patient are needed to assess the potential benefit for the individual. While 'across the board' results are useful from a population perspective, examination of patterns of treatment response during the course of follow up and for subpopulations of patients is required to make progress and solidify consensus on how to treat individual patients. For example, for several decades it has been known that the pattern of recurrence risk from time of diagnosis is different for estrogen receptor (ER)-negative and ER-positive disease. Assuming that ER status is accurately assessed and distinguishing absence of receptors from low, intermediate and high expression cohorts, one can recognize patterns of relapse risk that are early versus later during follow up. Treatments effective against ER-negative disease reduce the risk of early relapse, while those acting on ER-positive disease demonstrate effectiveness later during the course of follow up. Another example is HER2-positive disease, where a relatively high proportion of patients tend to relapse early, and treatments such as trastuzumab that reduce the risk of early relapse have demonstrated efficacy. For premenopausal patients with ER-positive disease, ovarian function suppression and endocrine effects of chemotherapy are effective to reduce the risk of late occurring relapses. Examining the influence of patient and disease-related factors on the patterns of recurrence over time and treatment responsiveness within subpopulations in multiple randomized trials can facilitate consensus on progress that has been made and identify areas for improving the care of patients with breast cancer.

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Year:  2009        PMID: 19914537     DOI: 10.1016/S0960-9776(09)70265-6

Source DB:  PubMed          Journal:  Breast        ISSN: 0960-9776            Impact factor:   4.380


  7 in total

1.  Optimal sequence of implied modalities in the adjuvant setting of breast cancer treatment: an update on issues to consider.

Authors:  Pelagia G Tsoutsou; Yazid Belkacemi; Joseph Gligorov; Abraham Kuten; Hamouda Boussen; Nuran Bese; Michael I Koukourakis
Journal:  Oncologist       Date:  2010-11-01

2.  Reproducibility of residual cancer burden for prognostic assessment of breast cancer after neoadjuvant chemotherapy.

Authors:  Florentia Peintinger; Bruno Sinn; Christos Hatzis; Constance Albarracin; Erinn Downs-Kelly; Jerzy Morkowski; Rebekah Gould; W Fraser Symmans
Journal:  Mod Pathol       Date:  2015-05-01       Impact factor: 7.842

3.  Whole tumor section quantitative image analysis maximizes between-pathologists' reproducibility for clinical immunohistochemistry-based biomarkers.

Authors:  Michael Barnes; Chukka Srinivas; Isaac Bai; Judith Frederick; Wendy Liu; Anindya Sarkar; Xiuzhong Wang; Yao Nie; Bryce Portier; Monesh Kapadia; Olcay Sertel; Elizabeth Little; Bikash Sabata; Jim Ranger-Moore
Journal:  Lab Invest       Date:  2017-08-14       Impact factor: 5.662

4.  A call to standardize preanalytic data elements for biospecimens.

Authors:  James A Robb; Margaret L Gulley; Patrick L Fitzgibbons; Mary F Kennedy; L Mark Cosentino; Kay Washington; Rajesh C Dash; Philip A Branton; Scott D Jewell; Rosanna L Lapham
Journal:  Arch Pathol Lab Med       Date:  2013-08-12       Impact factor: 5.534

Review 5.  Assessment of estrogen receptor low positive status in breast cancer: Implications for pathologists and oncologists.

Authors:  Nicola Fusco; Moira Ragazzi; Elham Sajjadi; Konstantinos Venetis; Roberto Piciotti; Stefania Morganti; Giacomo Santandrea; Giuseppe Nicolò Fanelli; Luca Despini; Marco Invernizzi; Bruna Cerbelli; Cristian Scatena; Carmen Criscitiello
Journal:  Histol Histopathol       Date:  2021-09-29       Impact factor: 2.303

6.  Quality assessment of estrogen receptor and progesterone receptor testing in breast cancer using a tissue microarray-based approach.

Authors:  T J A Dekker; S ter Borg; G K J Hooijer; S L Meijer; J Wesseling; J E Boers; E Schuuring; J Bart; J van Gorp; P Bult; S A Riemersma; C H M van Deurzen; H F B M Sleddens; W E Mesker; J R Kroep; V T H B M Smit; M J van de Vijver
Journal:  Breast Cancer Res Treat       Date:  2015-06-04       Impact factor: 4.872

7.  Integrating and validating automated digital imaging analysis of estrogen receptor immunohistochemistry in a fully digital workflow for clinical use.

Authors:  Saba Shafi; David A Kellough; Giovanni Lujan; Swati Satturwar; Anil V Parwani; Zaibo Li
Journal:  J Pathol Inform       Date:  2022-06-30
  7 in total

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