Ludwig Center at Harvard

Mahadevan, N.R., et al. Intrinsic immunogenicity of small cell lung carcinoma revealed by its cellular plasticity. Cancer Discov (2021).Abstract

Small cell lung carcinoma (SCLC) is highly mutated, yet durable response to immune checkpoint blockade (ICB) is rare. SCLC also exhibits cellular plasticity, which could influence its immunobiology. Here we discover that a distinct subset of SCLC uniquely upregulates MHC I, enriching for durable ICB benefit. In vitro modeling confirms epigenetic recovery of MHC I in SCLC following loss of neuroendocrine differentiation, which tracks with de-repression of STING. Transient EZH2 inhibition expands these non-neuroendocrine cells, which display intrinsic innate immune signaling and basally restored antigen presentation. Consistent with these findings, murine non-neuroendocrine SCLC tumors are rejected in a syngeneic model, with clonal expansion of immunodominant effector CD8 T cells. Therapeutically, EZH2 inhibition followed by STING agonism enhances T cell recognition and rejection of SCLC in mice. Together, these data identify MHC I as a novel biomarker of SCLC immune responsiveness and suggest novel immunotherapeutic approaches to co-opt SCLC's intrinsic immunogenicity.

Mehta, A.K., et al. Targeting immunosuppressive macrophages overcomes PARP inhibitor resistance in BRCA1-associated triple-negative breast cancer. Nat Cancer 2, 1, 66-82 (2021).Abstract

Despite objective responses to PARP inhibition and improvements in progression-free survival compared to standard chemotherapy in patients with BRCA-associated triple-negative breast cancer (TNBC), benefits are transitory. Using high dimensional single-cell profiling of human TNBC, here we demonstrate that macrophages are the predominant infiltrating immune cell type in BRCA-associated TNBC. Through multi-omics profiling we show that PARP inhibitors enhance both anti- and pro-tumor features of macrophages through glucose and lipid metabolic reprogramming driven by the sterol regulatory element-binding protein 1 (SREBP-1) pathway. Combined PARP inhibitor therapy with CSF-1R blocking antibodies significantly enhanced innate and adaptive anti-tumor immunity and extends survival in BRCA-deficient tumors and is mediated by CD8 T-cells. Collectively, our results uncover macrophage-mediated immune suppression as a liability of PARP inhibitor treatment and demonstrate combined PARP inhibition and macrophage targeting therapy induces a durable reprogramming of the tumor microenvironment, thus constituting a promising therapeutic strategy for TNBC.

Schneider, J.L., et al. The aging lung: Physiology, disease, and immunity. Cell (2021).Abstract

The population is aging at a rate never seen before in human history. As the number of elderly adults grows, it is imperative we expand our understanding of the underpinnings of aging biology. Human lungs are composed of a unique panoply of cell types that face ongoing chemical, mechanical, biological, immunological, and xenobiotic stress over a lifetime. Yet, we do not fully appreciate the mechanistic drivers of lung aging and why age increases the risk of parenchymal lung disease, fatal respiratory infection, and primary lung cancer. Here, we review the molecular and cellular aspects of lung aging, local stress response pathways, and how the aging process predisposes to the pathogenesis of pulmonary disease. We place these insights into context of the COVID-19 pandemic and discuss how innate and adaptive immunity within the lung is altered with age.

Yuan, B., et al. CellBox: Interpretable Machine Learning for Perturbation Biology with Application to the Design of Cancer Combination Therapy. Cell Syst 12, 2, 128-140.e4 (2021).Abstract

Systematic perturbation of cells followed by comprehensive measurements of molecular and phenotypic responses provides informative data resources for constructing computational models of cell biology. Models that generalize well beyond training data can be used to identify combinatorial perturbations of potential therapeutic interest. Major challenges for machine learning on large biological datasets are to find global optima in a complex multidimensional space and mechanistically interpret the solutions. To address these challenges, we introduce a hybrid approach that combines explicit mathematical models of cell dynamics with a machine-learning framework, implemented in TensorFlow. We tested the modeling framework on a perturbation-response dataset of a melanoma cell line after drug treatments. The models can be efficiently trained to describe cellular behavior accurately. Even though completely data driven and independent of prior knowledge, the resulting de novo network models recapitulate some known interactions. The approach is readily applicable to various kinetic models of cell biology. A record of this paper's Transparent Peer Review process is included in the Supplemental Information.

Muthuswamy, S.K. Self-organization in cancer: implications for histopathology, cancer cell biology, and metastasis. Cancer Cell (2021). Publisher's Version Abstract

Pathologists use histological features to classify tumors and assign site of origin for metastasis. How and why tumors organize the way they do and recreate their histological organization during metastasis is unknown. Here, I discuss the concept of “histostasis” conferring tumors a histological memory and hypothesize its implications for metastasis.

Explore

Participate

Featured Publications

Contact

Latest News