Oops, you're using an old version of your browser so some of the features on this page may not be displaying properly.

MINIMAL Requirements: Google Chrome 24+Mozilla Firefox 20+Internet Explorer 11Opera 15–18Apple Safari 7SeaMonkey 2.15-2.23

Poster display - Cocktail

1274 - MicroRNA based immune response signature identifies poor prognostic subgroup within ER negative breast cancers


24 Nov 2018


Poster display - Cocktail


Tumour Immunology;  Translational Research

Tumour Site

Breast Cancer


Savitha Rajarajan


Annals of Oncology (2018) 29 (suppl_9): ix13-ix20. 10.1093/annonc/mdy428


S. Rajarajan1, J. Prabhu1, A. Korlimarla1, M. Nair1, A. Alexander1, R. Kaluve1, H. Ps1, U. Raja1, R. Ramesh2, S. Patil3, S. Bs3, S. Ts1

Author affiliations

  • 1 Molecular Medicine, St. John's Research Institute, St. John's Medical College Hospital, 560034 - Bangalore/IN
  • 2 Surgical Oncology, St. John's Medical College Hospital, 560034 - Bangalore/IN
  • 3 Medical Oncology, Shankara CAncer hospital and research centre, 560034 - bangalore/IN


Login to access the resources on OncologyPRO.

If you do not have an ESMO account, please create one for free.

Abstract 1274


Tumor micro environment enabling an anti-tumor immune response is emerging as a key factor regulating the host response in multiple solid tumors including breast cancer. We had earlier identified immune related subtypes within triple negative breast cancers using unsupervised consensus clustering on gene expression profiles (A. Sadanandam et al, ESMO ASIA-2017). In this study, we have attempted to expand this sub typing in ER negative tumors using microRNA profiles.


Tumor infiltrating lymphocytes (TIL) were quantified microscopically in histological sections and categorised into dense and mild on 234 tumors from our cohort of breast cancer patients with a median follow up of 72 months. Expression levels of multiple miRs were estimated by quantitative real time PCR. To identify miRs capable of predicting immune response (IR), several binomial logistic regression models were examined using the dense infiltrate as the predictor to generate a probability score of IR. Tumors were divided into classes of high and low IR based on the mean probability score. Clinical parameters and survival were compared between the low and high IR groups. Prognostic ability of the IR score was validated using the METABRIC data set.


116/234 (50%) of the tumors had a dense immune infiltrate. Probability score of IR using best fitting model identified by the least Baysian information criterion identified only 3 miRs (miR-155, miR-107 and miR-29a) as the predictors, and the probability ranged from 0.23 to 0.78. 106 tumors not completely overlapping the ones with dense infiltrate were categorised as having a higher than mean IR score, and had better survival when compared to low IR score tumors (p-0.007). Disease free survival was significantly different (p-0.03) between high vs low IR groups within ER negative tumors in the METABRIC cohort.


The miR based identification of high IR not completely overlapping the histological dense infiltrate group suggests that it is not merely the density that is the final determinant of the effectiveness of the host immune response. The mechanisms of action are being investigated and may permit therapeutic interventions in the future.

Editorial acknowledgement

Clinical trial identification

Legal entity responsible for the study

Nadathur Estates Pvt. Ltd.


Nadathur Estates Pvt. Ltd.


All authors have declared no conflicts of interest.

This site uses cookies. Some of these cookies are essential, while others help us improve your experience by providing insights into how the site is being used.

For more detailed information on the cookies we use, please check our Privacy Policy.

Customise settings
  • Necessary cookies enable core functionality. The website cannot function properly without these cookies, and you can only disable them by changing your browser preferences.