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Breast Cancer (BC) is a major public health concern. Triple-negative BC (TNBC) have long been challenging to treat. Pembrolizumab (anti-PD1) with neoadjuvant chemotherapy has recently become the standard of care in early TNBC, yet ~30% of patients resist and have poor outcome. More effective immunotherapy (IT) strategies are needed.To address this, we will 1) validate biomarkers to identify patients that will not benefit from treatment, and 2) discover/validate alternative targets.The novelty and feasibility of the program reside in i) unique TNBC cohorts and already generated data with clinical information

ii) integrating innovative methodologies (scRNAseq, advanced spatial and computational biology) to identify biomarkers and resistance mechanism to IT and discover innate immune surveillance mechanisms that are overridden at preneoplastic stage

iii) validating novel targets and companion biomarkers, evaluating their impact on clinical outcome and exploring their biology in vivo/in vitro, iv) developing therapeutic antibodies against the validated targets, and v) involving clinicians and patients to validate the unmet ̽»¨¾«Ñ¡ need and facilitate transfer to care and acceptability of knowledge.The project relies on multidisciplinary and inter-sectorial collaborations between key opinion leaders, partners with expertise in TNBC clinical management, immuno-oncology, computational biology and clinical bioinformatics, drug development and patients and lay public interactions.

Tumors are not just pockets of cancer cells: you also find healthy cells from the tissue where the tumor grows and blood vessel cells. Tumors also contain immune cells whose task is to kill cancer cells, but which in some cases cannot intervene. The growth of a tumor or its rejection by the immune system depends on how these different cells organize themselves in space. But their organization is overwhelmingly complex: a tumor has millions to billions of cells that can perform dozens of different jobs in the tumor. Understanding tumor organization is like assembling an Ikea piece of furniture made of millions of parts of dozens of different types without building instructions. The aim of this project is to analyze many tumors to discover their common building plan. There is good reason to believe that there is a blueprint: healthy cells in tumors have evolved to cooperate, so there must be hidden order in the seemingly arbitrary chaos of tumor architecture. To discover this hidden order, we will develop a new microscopy method to obtain data on how cells are organized in tumors. We then analyze the data using mathematical techniques from ecology because ecology has a long tradition of studying how different species organize themselves in their environment. With this project, we hope to identify the basic building blocks of tumors and how these building blocks are assembled piece by piece to build the entire tumor. We hope to discover new important building blocks that could not be discovered without our new microscopy technique and mathematical method. Vii also hopes that our innovations in microscopy and mathematical methods for deciphering the blueprint of tumors will help other cancer researchers understand tumor architecture as well as help doctors repurpose successful treatments of tumors to other tumors with similar blueprints.

Cancer is still the second most common cause of death in many countries. This is largely due to the cancer's resistance to therapy. Resistance to therapy occurs because the millions or billions of cancer cells that make up the tumor differ slightly from each other. For example, they carry different mutations in the DNA. If we fail to eliminate 1% of the cancer cells, these cells will grow and divide. As a result, we transition to the second treatment line and another minority of cells begin to grow. The scenario is repeated until we have not long left any treatment options. To solve this problem, it would be helpful if we could predict how cancer cells will adapt to a given treatment. If we knew how cancer could most likely be adapted, we could combine a treatment targeting 99% of the cancer cells, with a second treatment targeting 1% of the cancer cells likely to adapt to the first treatment. With this combined therapy, we can reduce the risk of a minority of cancer cells surviving the treatment. In this project we will examine the potential of this strategy. We should first ask what is the main reason why cancer cells differ from each other before treatment. Secondly, we should treat cancer cells derived from 25 different breast tumors with tamoxifen and observe how they adapt to this drug. Finally, we will use these observations to develop a mathematical model to predict how our cancer cells adapt to the treatment and test whether the treatment of these adjustments reduces the risk of resistance. If this succeeds, this project will form the basis for incorporating these ideas into preclinical models and other cancer drugs.