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Purpose: Classic Hodgkin lymphoma (cHL) is a B-cell lymphoma that occurs primarily in young adults and, less frequently, in elderly individuals. A hallmark of cHL is the exceptional scarcity (1%-5%) of the malignant Hodgkin Reed-Sternberg (HRS) cells within a network of nonmalignant immune cells. Molecular determinants governing the relationship between HRS cells and their proximal microenvironment remain largely unknown., Experimental Design: We performed spatially resolved multiplexed protein imaging and transcriptomic sequencing to characterize HRS cell states, cellular neighborhoods, and gene expression signatures of 23.6 million cells from 36 newly diagnosed Epstein-Barr virus (EBV)-positive and EBV-negative cHL tumors., Results: We show that MHC-I expression on HRS cells is associated with immune-inflamed neighborhoods containing CD8+ T cells, MHC-II+ macrophages, and immune checkpoint expression (i.e., PD1 and VISTA). We identified spatial clustering of HRS cells, consistent with the syncytial variant of cHL, and its association with T-cell-excluded neighborhoods in a subset of EBV-negative tumors. Finally, a subset of both EBV-positive and EBV-negative tumors contained regulatory T-cell-high neighborhoods harboring HRS cells with augmented proliferative capacity., Conclusions: Our study links HRS cell properties with distinct immunophenotypes and potential immune escape mechanisms in cHL., (©2024 The Authors; Published by the American Association for Cancer Research.)

Drug resistance is the major cause of therapeutic failure in high-grade serous ovarian cancer (HGSOC). Yet, the mechanisms by which tumors evolve to drug resistant states remains largely unknown. To address this, we aimed to exploit clone-specific genomic structural variations by combining scaled single-cell whole genome sequencing with longitudinally collected cell-free DNA (cfDNA), enabling clonal tracking before, during and after treatment. We developed a cfDNA hybrid capture, deep sequencing approach based on leveraging clone-specific structural variants as endogenous barcodes, with orders of magnitude lower error rates than single nucleotide variants in ctDNA (circulating tumor DNA) detection, demonstrated on 19 patients at baseline. We then applied this to monitor and model clonal evolution over several years in ten HGSOC patients treated with systemic therapy from diagnosis through recurrence. We found drug resistance to be polyclonal in most cases, but frequently dominated by a single high-fitness and expanding clone, reducing clonal diversity in the relapsed disease state in most patients. Drug-resistant clones frequently displayed notable genomic features, including high-level amplifications of oncogenes such as CCNE1 , RAB25 , NOTCH3 , and ERBB2 . Using a population genetics Wright-Fisher model, we found evolutionary trajectories of these features were consistent with drug-induced positive selection. In select cases, these alterations impacted selection of secondary lines of therapy with positive patient outcomes. For cases with matched single-cell RNA sequencing data, pre-existing and genomically encoded phenotypic states such as upregulation of EMT and VEGF were linked to drug resistance. Together, our findings indicate that drug resistant states in HGSOC pre-exist at diagnosis and lead to dramatic clonal expansions that alter clonal composition at the time of relapse. We suggest that combining tumor single cell sequencing with cfDNA enables clonal tracking in patients and harbors potential for evolution-informed adaptive treatment decisions., Competing Interests: B.W. reports grant funding by Repare Therapeutics paid to the institution, outside the submitted work, and employment of a direct family member at AstraZeneca. C.A. reports grants from Clovis, Genentech, AbbVie and AstraZeneca and personal fees from Tesaro, Eisai/Merck, Mersana Therapeutics, Roche/Genentech, Abbvie, AstraZeneca/Merck and Repare Therapeutics, outside the scope of the submitted work. C.A. reports clinical trial funding to the institution from Abbvie, AstraZeneca, and Genentech/Roche; participation on a data safety monitoring board or advisory board in AstraZeneca and Merck; unpaid membership of the GOG Foundation Board of Directors and the NRG Oncology Board of Directors. M.F.B reports consulting fees (Eli Lilly, AstraZeneca, Paige.AI), Research Support (Boundless Bio) and Intellectual Property Rights (SOPHiA Genetics). BL reports Intellectual propery rights (SOPHiA Genetics) and licensing royalties (BioLegend/Revvity). C.F. reports research funding to the institution from Merck, AstraZeneca, Genentech/Roche, Bristol Myers Squibb, and Daiichi; uncompensated membership of a scientific advisory board for Merck and Genentech; and is a consultant for OncLive, Aptitude Health, Bristol Myers Squibb and Seagen, all outside the scope of this manuscript. D.S.C. reports membership of the medical advisory board of Verthermia Acquio Inc and Biom’up, is a paid speaker for AstraZeneca, and holds stock of Doximity, Moderna, and BioNTech. D.Z. reports institutional grants from Merck, Genentech, AstraZeneca, Plexxikon, and Synthekine, and personal fees from AstraZeneca, Xencor, Memgen, Takeda, Astellas, Immunos, Tessa Therapeutics, Miltenyi, and Calidi Biotherapeutics. D.Z. own a patent on use of oncolytic Newcastle Disease Virus for cancer therapy. N.A.-R. reports grants to the institution from Stryker/Novadaq and GRAIL, outside the submitted work. S.P.S. reports research funding from AstraZeneca and Bristol Myers Squibb, outside the scope of this work.

Whole-genome doubling (WGD) is a critical driver of tumor development and is linked to drug resistance and metastasis in solid malignancies. Here, we demonstrate that WGD is an ongoing mutational process in tumor evolution. Using single-cell whole-genome sequencing, we measured and modeled how WGD events are distributed across cellular populations within tumors and associated WGD dynamics with properties of genome diversification and phenotypic consequences of innate immunity. We studied WGD evolution in 65 high-grade serous ovarian cancer (HGSOC) tissue samples from 40 patients, yielding 29,481 tumor cell genomes. We found near-ubiquitous evidence of WGD as an ongoing mutational process promoting cell-cell diversity, high rates of chromosomal missegregation, and consequent micronucleation. Using a novel mutation-based WGD timing method, doubleTime , we delineated specific modes by which WGD can drive tumor evolution: (i) unitary evolutionary origin followed by significant diversification, (ii) independent WGD events on a pre-existing background of copy number diversity, and (iii) evolutionarily late clonal expansions of WGD populations. Additionally, through integrated single-cell RNA sequencing and high-resolution immunofluorescence microscopy, we found that inflammatory signaling and cGAS-STING pathway activation result from ongoing chromosomal instability and are restricted to tumors that remain predominantly diploid. This contrasted with predominantly WGD tumors, which exhibited significant quiescent and immunosuppressive phenotypic states. Together, these findings establish WGD as an evolutionarily 'active' mutational process that promotes evolvability and dysregulated immunity in late stage ovarian cancer.

Programmed death-ligand 1 (PD-L1) IHC is the most commonly used biomarker for immunotherapy response. However, quantification of PD-L1 status in pathology slides is challenging. Neither manual quantification nor a computer-based mimicking of manual readouts is perfectly reproducible, and the predictive performance of both approaches regarding immunotherapy response is limited. In this study, we developed a deep learning (DL) method to predict PD-L1 status directly from raw IHC image data, without explicit intermediary steps such as cell detection or pigment quantification. We trained the weakly supervised model on PD-L1-stained slides from the non-small cell lung cancer (NSCLC)-Memorial Sloan Kettering (MSK) cohort (N = 233) and validated it on the pan-cancer-Vall d'Hebron Institute of Oncology (VHIO) cohort (N = 108). We also investigated the performance of the model to predict response to immune checkpoint inhibitors (ICI) in terms of progression-free survival. In the pan-cancer-VHIO cohort, the performance was compared with tumor proportion score (TPS) and combined positive score (CPS). The DL model showed good performance in predicting PD-L1 expression (TPS ≥ 1%) in both NSCLC-MSK and pan-cancer-VHIO cohort (AUC 0.88 ± 0.06 and 0.80 ± 0.03, respectively). The predicted PD-L1 status showed an improved association with response to ICIs [HR: 1.5 (95% confidence interval: 1-2.3), P = 0.049] compared with TPS [HR: 1.4 (0.96-2.2), P = 0.082] and CPS [HR: 1.2 (0.79-1.9), P = 0.386]. Notably, our explainability analysis showed that the model does not just look at the amount of brown pigment in the IHC slides, but also considers morphologic factors such as lymphocyte conglomerates. Overall, end-to-end weakly supervised DL shows potential for improving patient stratification for cancer immunotherapy by analyzing PD-L1 IHC, holistically integrating morphology and PD-L1 staining intensity., Significance: The weakly supervised DL model to predict PD-L1 status from raw IHC data, integrating tumor staining intensity and morphology, enables enhanced patient stratification in cancer immunotherapy compared with traditional pathologist assessment., (© 2024 The Authors; Published by the American Association for Cancer Research.)