Neutralization of PD-L2 is Essential for Overcoming Immune Checkpoint Blockade Resistance in Ovarian Cancer

PURPOSE

Ovarian cancer represents a major clinical hurdle for immune checkpoint blockade (ICB), with reported low patient response rates. We found that the immune checkpoint ligand PD-L2 is robustly expressed in patient samples of ovarian cancers and other malignancies exhibiting suboptimal response to ICB but not in cancers that are ICB sensitive. Therefore, we hypothesize that PD-L2 can facilitate immune escape from ICB through incomplete blockade of the PD-1 signaling pathway.

EXPERIMENTAL DESIGN

We engineered a soluble form of the PD-1 receptor (sPD-1) capable of binding and neutralizing both PD-L2 and PD-L1 with ×200 and ×10,000 folds improvement in binding affinity over wild-type PD-1 leading to superior inhibition of ligand-mediated PD-1 activities.

RESULTS

Both in vitro and in vivo analyses performed in this study demonstrated that the high-affinity sPD-1 molecule is superior at blocking both PD-L1- and PD-L2-mediated immune evasion and reducing tumor growth in immune-competent murine models of ovarian cancer.

CONCLUSIONS

The data presented in this study provide justification for using a dual targeting, high-affinity sPD-1 receptor as an alternative to PD-1 or PD-L1 therapeutic antibodies for achieving superior therapeutic efficacy in cancers expressing both PD-L2 and PD-L1.

Disparity in the quality of COVID-19 data reporting across India

BACKGROUND

Transparent and accessible reporting of COVID-19 data is critical for public health efforts. Each Indian state has its own mechanism for reporting COVID-19 data, and the quality of their reporting has not been systematically evaluated. We present a comprehensive assessment of the quality of COVID-19 data reporting done by the Indian state governments between 19 May and 1 June, 2020.

METHODS

We designed a semi-quantitative framework with 45 indicators to assess the quality of COVID-19 data reporting. The framework captures four key aspects of public health data reporting - availability, accessibility, granularity, and privacy. We used this framework to calculate a COVID-19 Data Reporting Score (CDRS, ranging from 0-1) for each state.

RESULTS

Our results indicate a large disparity in the quality of COVID-19 data reporting across India. CDRS varies from 0.61 (good) in Karnataka to 0.0 (poor) in Bihar and Uttar Pradesh, with a median value of 0.26. Ten states do not report data stratified by age, gender, comorbidities or districts. Only ten states provide trend graphics for COVID-19 data. In addition, we identify that Punjab and Chandigarh compromised the privacy of individuals under quarantine by publicly releasing their personally identifiable information. The CDRS is positively associated with the state's sustainable development index for good health and well-being (Pearson correlation: r=0.630,p=0.0003).

CONCLUSIONS

Our assessment informs the public health efforts in India and serves as a guideline for pandemic data reporting. The disparity in CDRS highlights three important findings at the national, state, and individual level. At the national level, it shows the lack of a unified framework for reporting COVID-19 data in India, and highlights the need for a central agency to monitor or audit the quality of data reporting done by the states. Without a unified framework, it is difficult to aggregate the data from different states, gain insights, and coordinate an effective nationwide response to the pandemic. Moreover, it reflects the inadequacy in coordination or sharing of resources among the states. The disparate reporting score also reflects inequality in individual access to public health information and privacy protection based on the state of residence.

Unearthing the hidden world of roots: Root biomass and architecture differ among species within the same guild

The potential benefits of planting trees have generated significant interest with respect to sequestering carbon and restoring other forest based ecosystem services. Reliable estimates of carbon stocks are pivotal for understanding the global carbon balance and for promoting initiatives to mitigate CO₂ emissions through forest management. There are numerous studies employing allometric regression models that convert inventory into aboveground biomass (AGB) and carbon (C). Yet the majority of allometric regression models do not consider the root system nor do these equations provide detail on the architecture and shape of different species. The root system is a vital piece toward understanding the hidden form and function roots play in carbon accumulation, nutrient and plant water uptake, and groundwater infiltration. Work that estimates C in forests as well as models that are used to better understand the hydrologic function of trees need better characterization of tree roots. We harvested 40 trees of six different species, including their roots down to 2 mm in diameter and created species-specific and multi-species models to calculate aboveground (AGB), coarse root belowground biomass (BGB), and total biomass (TB). We also explore the relationship between crown structure and root structure. We found that BGB contributes ~27.6% of a tree’s TB, lateral roots extend over 1.25 times the distance of crown extent, root allocation patterns varied among species, and that AGB is a strong predictor of TB. These findings highlight the potential importance of including the root system in C estimates and lend important insights into the function roots play in water cycling.