Project Summary/abstract
Triple negative breast cancer (TNBC) is the most aggressive breast cancer subtype and poses a clinical
challenge due to the lack of tools to predict patients who are at risk of death due to disease. TNBCs subtypes
were presumed to be a step forward, however, recent randomized clinical trials have failed to document
differential impact of therapy within TNBC subtypes. Immunotherapies will benefit a small subgroup (~7 out of
100 women) of TNBC patients, but are associated with 15-30% increase in grade 3 immune toxicities. Currently,
all patients with TNBCs are offered chemotherapy-based regimens either after surgery (70%) or less often
(~30%) before surgery (neo-adjuvant). For the 30% of TNBC patients prescribed neo-adjuvant chemotherapy in
TNBC patients presence or absence of residual disease has been used for optimizing escalation or de-escalation
strategies. Studies such as the SWOG 2212 (SCARLET) are being proposed to de-escalate neo-adjuvant
chemotherapy in TNBC based on TILs with residual disease as the endpoint. However, for the 70% of TNBC
patients treated with adjuvant chemotherapy, there are no established prognostic tools, apart for TILs, for de-
escalating therapies. Better tools are necessary to identify potentially lethal TNBC cases with high accuracy. The
overall objective of this grant is to develop novel tools for prediction of risk of lethality in TNBC using a multi-
modal approach. The central hypothesis is that accuracy of lethality prediction can be improved by a multi-scale
approach comprising novel artificial intelligence (AI)-based tools of mammographic images and pathology
including spatial immune analysis. The following specific aims are proposed: Aim 1) To generate a novel model
of mammographic radiomic features to determine probability of TNBC lethality; Aim 2) To establish the role of
generative adversarial network (GAN) augmented deep learning (DL) model from digital H&E images for
predicting probability of lethality; Aim 3) To profile immune cell topology and spatial relationships and their
contribution to risk of lethality; Aim 4) To integrate multimodal data models for identification of risk of lethality in
TNBCs. A late fusion model will integrate the clinicopathological features with probabilities obtained from
pathomics and radiomics and immune data to develop the CaPRI prediction score for assessing lethality of
TNBC that will stratify patients based on their risk. The score will provide evidence-based rationale for escalating
or deescalating therapy in TNBC. The developed tool, CaPRI score, will be based on multi-modal analysis and
integrate them will clinicopathological features such as age, race and tumor size. The continuous CaPRI score
could be used either as a continuous measure of risk or in a categorical manner (high or low risk). High-risk
patients, like patients with residual cancer after neoadjuvant chemotherapy, would be candidates for escalation
of therapy. Similarly, patients with low risk could be candidates for de-escalation.