Prediction of the P. falciparum target space relevant to malaria drug discovery.

Malaria is still one of the most devastating infectious diseases, affecting hundreds of millions of patients worldwide. Even though there are several established drugs in clinical use for malaria treatment, there is an urgent need for new drugs acting through novel mechanisms of action due to the rapid development of resistance. Resistance emerges when the parasite manages to mutate the sequence of the drug targets to the extent that the protein can still perform its function in the parasite but can no longer be inhibited by the drug, which then becomes almost ineffective. The design of a new generation of malaria drugs targeting multiple essential proteins would make it more difficult for the parasite to develop full resistance without lethally disrupting some of its vital functions. The challenge is then to identify which set of Plasmodium falciparum proteins, among the millions of possible combinations, can be targeted at the same time by a given chemotype. To do that, we predicted first the targets of the close to 20,000 antimalarial hits identified recently in three independent phenotypic screening campaigns. All targets predicted were then projected onto the genome of P. falciparum using orthologous relationships. A total of 226 P. falciparum proteins were predicted to be hit by at least one compound, of which 39 were found to be significantly enriched by the presence and degree of affinity of phenotypically active compounds. The analysis of the chemically compatible target combinations containing at least one of those 39 targets led to the identification of a priority set of 64 multi-target profiles that can set the ground for a new generation of more robust malaria drugs.

HER2 quantitative continuous scoring for accurate patient selection in HER2 negative trastuzumab deruxtecan treated breast cancer.

Many targeted cancer therapies rely on biomarkers assessed by scoring of immunohistochemically (IHC)-stained tissue, which is subjective, semiquantitative, and does not account for expression heterogeneity. We describe an image analysis-based method for quantitative continuous scoring (QCS) of digital whole-slide images acquired from baseline human epidermal growth factor receptor 2 (HER2) IHC-stained breast cancer tissue. Candidate signatures for patient stratification using QCS of HER2 expression on subcellular compartments were identified, addressing the spatial distribution of tumor cells and tumor-infiltrating lymphocytes. Using data from trastuzumab deruxtecan-treated patients with HER2-positive and HER2-negative breast cancer from a phase 1 study (NCT02564900; DS8201-A-J101; N = 151), QCS-based patient stratification showed longer progression-free survival (14.8 vs 8.6 months) with higher prevalence of patient selection (76.4 vs 56.9%) and a better cross-validated log-rank p value (0.026 vs 0.26) than manual scoring based on the American Society of Clinical Oncology / College of American Pathologists guidelines. QCS-based features enriched the HER2-negative subgroup by correctly predicting 20 of 26 responders.

MiniMuDS: a new optimizer using knowledge-based potentials improves scoring of docking solutions.

In small molecule docking, the scoring and ranking of generated conformations is an important, though still not a completely resolved problem. Rescoring schemes often improve the quality of the obtained rankings. It is known that a local optimization is essential before a valid rescore value can be calculated. Here, we present a method that improves rescoring results obtained with the DrugScore function due to a new optimization technique. The method implements a more sophisticated search algorithm compared to the classic local optimization procedures used in this context. We validated the proposed method on a set of 192 protein-ligand complexes. Results show substantial improvements compared to original docking results with success rates increased by up to 10% for top scored solutions below 2 Å root-mean-square deviation to the native state and up to 18% increase below 1 Å, respectively.

Automated image analysis of NSCLC biopsies to predict response to anti-PD-L1 therapy.

BACKGROUND: Immune checkpoint therapies (ICTs) targeting the programmed cell death-1 (PD1)/programmed cell death ligand-1 (PD-L1) pathway have improved outcomes for patients with non-small cell lung cancer (NSCLC), particularly those with high PD-L1 expression. However, the predictive value of manual PD-L1 scoring is imperfect and alternative measures are needed. We report an automated image analysis solution to determine the predictive and prognostic values of the product of PD-L1+ cell and CD8+ tumor infiltrating lymphocyte (TIL) densities (CD8xPD-L1 signature) in baseline tumor biopsies. METHODS: Archival or fresh tumor biopsies were analyzed for PD-L1 and CD8 expression by immunohistochemistry. Samples were collected from 163 patients in Study 1108/NCT01693562, a Phase 1/2 trial to evaluate durvalumab across multiple tumor types, including NSCLC, and a separate cohort of 199 non-ICT- patients. Digital images were automatically scored for PD-L1+ and CD8+ cell densities using customized algorithms applied with Developer XD™ 2.7 software. RESULTS: For patients who received durvalumab, median overall survival (OS) was 21.0 months for CD8xPD-L1 signature-positive patients and 7.8 months for signature-negative patients (p = 0.00002). The CD8xPD-L1 signature provided greater stratification of OS than high densities of CD8+ cells, high densities of PD-L1+ cells, or manually assessed tumor cell PD-L1 expression ≥25%. The CD8xPD-L1 signature did not stratify OS in non-ICT patients, although a high density of CD8+ cells was associated with higher median OS (high: 67 months; low: 39.5 months, p = 0.0009) in this group. CONCLUSIONS: An automated CD8xPD-L1 signature may help to identify NSCLC patients with improved response to durvalumab therapy. Our data also support the prognostic value of CD8+ TILS in NSCLC patients who do not receive ICT. TRIAL REGISTRATION: ClinicalTrials.gov identifier: NCT01693562 . Study code: CD-ON-MEDI4736-1108. Interventional study (ongoing but not currently recruiting). Actual study start date: August 29, 2012. Primary completion date: June 23, 2017 (final data collection date for primary outcome measure).

MammaPrint versus EndoPredict: Poor correlation in disease recurrence risk classification of hormone receptor positive breast cancer.

INTRODUCTION: Correct risk assessment of disease recurrence in patients with early breast cancer is critically important to detect patients who may be spared adjuvant chemotherapy. In clinical practice this is increasingly done based on the results of gene expression assays. In the present study we compared the concordance of the 70-gene signature MammaPrint (MP) with the 12 gene assay EndoPredict (EP). METHODS: Representative tissue of 48 primary tumours was analysed with the MP during routine diagnostic purposes. Corresponding formalin-fixed, paraffin-embedded tissue was thereafter analysed by the EP test. Risk categories of both tests were compared. RESULTS: 41 of 48 tumours could be directly compared by both tests. Of the 17 MP low risk cases, only 9 were considered low risk by EP (53% agreement) and of the 24 MP high risk cases, 18 were high risk by EP (75% agreement). Discrepancies occurred in 14 of 41 cases (34.1%). There was only a weak and non-significant correlation between the MP and EP test with an overall concordance of only 66%. The original therapeutic recommendation was based on the MP and would have been changed in 38% of the patients following EP test results. 4 patients developed distant metastases. The respective tumours of these patients were all classified as high risk by the EP, but only 3 were classified as high risk by the MP. CONCLUSION: Both tests resulted in different treatment recommendations for a significant proportion of patients and cannot be used interchangeably. The results underscore the urgent need for further comparative analyses of multi-genomic tests to avoid misclassification of disease recurrence risk in breast cancer patients.