Progress and challenges for the machine learning-based design of fit-for-purpose monoclonal antibodies

Although the therapeutic efficacy and commercial success of monoclonal antibodies (mAbs) are tremendous, the design and discovery of new candidates remain a time and cost-intensive endeavor. In this regard, progress in the generation of data describing antigen binding and developability, computational methodology, and artificial intelligence may pave the way for a new era of in silico on-demand immunotherapeutics design and discovery. Here, we argue that the main necessary machine learning (ML) components for an in silico mAb sequence generator are: understanding of the rules of mAb-antigen binding, capacity to modularly combine mAb design parameters, and algorithms for unconstrained parameter-driven in silico mAb sequence synthesis. We review the current progress toward the realization of these necessary components and discuss the challenges that must be overcome to allow the on-demand ML-based discovery and design of fit-for-purpose mAb therapeutic candidates

Comprehensive Profiling of Genomic and Transcriptomic Differences between Risk Groups of Lung Adenocarcinoma and Lung Squamous Cell Carcinoma

Lung cancer is the second most frequently diagnosed cancer type and responsible for the highest number of cancer deaths worldwide. Lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) are subtypes of non-small-cell lung cancer which has the highest frequency of lung cancer cases. We aimed to analyze genomic and transcriptomic variations including simple nucleotide variations (SNVs), copy number variations (CNVs) and differential expressed genes (DEGs) in order to find key genes and pathways for diagnostic and prognostic prediction for lung adenocarcinoma and lung squamous cell carcinoma. We performed a univariate Cox model and then lasso-regularized Cox model with leave-one-out cross-validation using The Cancer Genome Atlas (TCGA) gene expression data in tumor samples. We generated 35- and 33-gene signatures for prognostic risk prediction based on the overall survival time of the patients with LUAD and LUSC, respectively. When we clustered patients into high- and low-risk groups, the survival analysis showed highly significant results with high prediction power for both training and test datasets. Then, we characterized the differences including significant SNVs, CNVs, DEGs, active subnetworks, and the pathways. We described the results for the risk groups and cancer subtypes separately to identify specific genomic alterations between both high-risk groups and cancer subtypes. Both LUAD and LUSC high-risk groups have more downregulated immune pathways and upregulated metabolic pathways. On the other hand, low-risk groups have both up- and downregulated genes on cancer-related pathways. Both LUAD and LUSC have important gene alterations such as CDKN2A and CDKN2B deletions with different frequencies. SOX2 amplification occurs in LUSC and PSMD4 amplification in LUAD. EGFR and KRAS mutations are mutually exclusive in LUAD samples. EGFR, MGA, SMARCA4, ATM, RBM10, and KDM5C genes are mutated only in LUAD but not in LUSC. CDKN2A, PTEN, and HRAS genes are mutated only in LUSC samples. The low-risk groups of both LUAD and LUSC tend to have a higher number of SNVs, CNVs, and DEGs. The signature genes and altered genes have the potential to be used as diagnostic and prognostic biomarkers for personalized oncology

IRF6 Is Involved in the Regulation of Cell Proliferation and Transformation in MCF10A Cells Downstream of Notch Signaling.

IRF6, a member of Interferon Regulatory Factors (IRF) family, is involved in orofacial and epidermal development. In breast cancer cell lines ectopic expression of IRF6 reduces cell numbers suggesting a role as negative regulator of cell cycle. IRF6 is a direct target of canonical Notch signaling in keratinocyte differentiation. Notch is involved in luminal cell fate determination and stem cell regulation in the normal breast and is implicated as an oncogene in breast cancer. Notch activation is sufficient to induce proliferation and transformation in non-tumorigenic breast epithelial cell line, MCF10A. ΔNp63, which is downregulated by Notch activation in the breast, regulates IRF6 expression in keratinocytes. In this report, we investigate Notch-IRF6 and ΔNp63-IRF6 interactions in MCF10A and MDA MB 231 cells. We observed that in these cells, IRF6 expression is partially regulated by canonical Notch signaling and ΔNp63 downregulation. Furthermore, we demonstrate that IRF6 abrogation impairs Notch-induced proliferation and transformation in MCF10A cells. Thus, we confirm the previous findings by showing a tissue independent regulation of IRF6 by Notch signaling, and extend them by proposing a context dependent role for IRF6, which acts as a positive regulator of proliferation and transformation in MCF10A cells downstream of Notch signaling

IRF6 is involved in the regulation of cell proliferation and transformation in MCF10A cells downstream of notch signaling

IRF6, a member of Interferon Regulatory Factors (IRF) family, is involved in orofacial and epidermal development. In breast cancer cell lines ectopic expression of IRF6 reduces cell numbers suggesting a role as negative regulator of cell cycle. IRF6 is a direct target of canonical Notch signaling in keratinocyte differentiation. Notch is involved in luminal cell fate determination and stem cell regulation in the normal breast and is implicated as an oncogene in breast cancer. Notch activation is sufficient to induce proliferation and transformation in non-tumorigenic breast epithelial cell line, MCF10A. ΔNp63, which is downregulated by Notch activation in the breast, regulates IRF6 expression in keratinocytes. In this report, we investigate Notch-IRF6 and ΔNp63-IRF6 interactions in MCF10A and MDA MB 231 cells. We observed that in these cells, IRF6 expression is partially regulated by canonical Notch signaling and ΔNp63 downregulation. Furthermore, we demonstrate that IRF6 abrogation impairs Notch-induced proliferation and transformation in MCF10A cells. Thus, we confirm the previous findings by showing a tissue independent regulation of IRF6 by Notch signaling, and extend them by proposing a context dependent role for IRF6, which acts as a positive regulator of proliferation and transformation in MCF10A cells downstream of Notch signaling.Scientific and Technological Research Council of Turkey (110T895); Izmir Institute of Technolog

Notch inhibition reduces IRF6 expression in MDA MB 231 cells.

<p>Notch signaling was inhibited via overexpression of dominant negative mastermind (DNMM) (A and B) or silencing of Notch signaling mediator RBPjκ/CSL via shRNA (shCSL) (C and D). Relative mRNA expression levels of Notch target genes HEY1 and HEY2 and of IRF6 (A) and protein levels of IRF6 (B) 48 hours after infection with control (grey bars) or DNMM (black bars) expressing retrovirus (Left: representative western image, Right: Quantification of the protein bands). Relative mRNA expression levels of Notch target genes HEY1 and HEY2 and of IRF6 (C) and protein levels of IRF6 (D) 48 hours after infection with control (grey bars) or shCSL (black bars) expressing retrovirus (Left: representative western image, Right: Quantification of the protein bands). Values represent mean±S.D. of three independent experiments. (p values: *: <0.05, **: <0.004, ***: <0.0003).</p

IRF6 is required for Notch induced transformation.

<p>(A) Photomicrographs of MCF10A cells infected with (i) two control viruses, (ii) control and IRF6 shRNA (shIRF6) expressing viruses, (iii) NICD and control shRNA expressing viruses or (iv) NICD and shIRF6 expressing viruses and grown in soft agar for 8 weeks. Arrows indicate representative colonies that are in focus and counted. Scale bar: 500 μm. Number of colonies (bigger than 30 μm in diameter) per well (B) and average colony size (C) are shown for each condition. Values represent mean±S.D. of three independent experiments. (p values: *: <0.03, **: <0.004).</p

Notch activation induces IRF6 expression in MCF10A cells.

<p>Relative mRNA expression levels of (A) Notch target genes HEY1 and HEY2 and (B) IRF6 48 hours after infection with control (grey bars) or NICD expressing (black bars) retrovirus. (C) IRF6 protein levels 48 hours after infection with control or NICD expressing retrovirus (Left: representative western image, Right: Quantification of the protein bands). Values represent mean±S.D. of three independent experiments. (p values: *: <0.05, **: <0.02, ***: <0.003).</p

IRF6 is required for Notch induced cell proliferation.

<p>(A) Relative mRNA expression levels of Notch target gene HEY2 and of IRF6 in MCF10A cells infected with (i) two control viruses, (ii) control and IRF6 shRNA (shIRF6) expressing viruses, (iii) NICD and control shRNA expressing viruses or (iv) NICD and shIRF6 expressing viruses. (B) Representative dot plots of BrdU FACS analysis. R1 population (upper panel) were analyzed for BrdU-APC signal. R2 population (lower panel) shows events positive for BrdU-APC signal. (C) BrdU positive cell percentage in MCF10A cells, infected as indicated above, after 4 hours of BrdU incorporation. (D) MTT assay values indicating cell viability of MCF10A cells infected as indicated above. Values represent mean±S.D. of three independent experiments. (p values: *: < 0.05, **: <0.006).</p