A transcriptionally and functionally distinct PD-1⁺ CD8⁺ T cell pool with predictive potential in non-small-cell lung cancer treated with PD-1 blockade.

Evidence from mouse chronic viral infection models suggests that CD8 <sup>+</sup> T cell subsets characterized by distinct expression levels of the receptor PD-1 diverge in their state of exhaustion and potential for reinvigoration by PD-1 blockade. However, it remains unknown whether T cells in human cancer adopt a similar spectrum of exhausted states based on PD-1 expression levels. We compared transcriptional, metabolic and functional signatures of intratumoral CD8 <sup>+</sup> T lymphocyte populations with high (PD-1 <sup>T</sup> ), intermediate (PD-1 <sup>N</sup> ) and no PD-1 expression (PD-1 <sup>-</sup> ) from non-small-cell lung cancer patients. PD-1 <sup>T</sup> T cells showed a markedly different transcriptional and metabolic profile from PD-1 <sup>N</sup> and PD-1 <sup>-</sup> lymphocytes, as well as an intrinsically high capacity for tumor recognition. Furthermore, while PD-1 <sup>T</sup> lymphocytes were impaired in classical effector cytokine production, they produced CXCL13, which mediates immune cell recruitment to tertiary lymphoid structures. Strikingly, the presence of PD-1 <sup>T</sup> cells was strongly predictive for both response and survival in a small cohort of non-small-cell lung cancer patients treated with PD-1 blockade. The characterization of a distinct state of tumor-reactive, PD-1-bright lymphocytes in human cancer, which only partially resembles that seen in chronic infection, provides potential avenues for therapeutic intervention

Tumor infiltrating lymphocytes in lymph node metastases of stage III melanoma correspond to response and survival in nine patients treated with ipilimumab at the time of stage IV disease.

Prognosis of metastatic melanoma improved with the development of checkpoint inhibitors. The role of tumor infiltrating lymphocytes (TILs) in lymph node metastases of stage III melanoma remains unclear. We retrospectively characterized TILs in primary melanomas and matched lymph node metastases (stage III melanoma) of patients treated with the checkpoint inhibitor ipilimumab. Tumor infiltrating lymphocytes were characterized for CD3, CD4, and CD8 expressions by immunohistochemistry. 4/9 patients (44%) responded to treatment with ipilimumab (1 complete and 2 partial remissions, 1 stable disease). All responders exhibited CD4 and CD8 T-cell infiltration in their lymph node metastases, whereas all non-responders did not show an infiltration of the lymph node metastasis with TILs. The correlation between the presence and absence of TILs in responders vs. non-responders was statistically significant (p = 0.008). Median distant metastases free survival, i.e., progression from stage III to stage IV melanoma, was similar in responders and non-responders (22.1 vs. 19.3 months; p = 0.462). Median progression free and overall survival show a trend in favor of the patients having TIL rich lymph node metastases (6.8 vs. 3.3 months, p = 0.09; and all alive at last follow-up vs. 8.2 months, respectively, p = 0.08). Our data suggest a correlation between the T-cell infiltration of the lymph node metastases in stage III melanoma and the response to ipilimumab once these patients progress to stage IV disease

Digital image analysis and artificial intelligence in pathology diagnostics-the Swiss view

Digital pathology (DP) is increasingly entering routine clinical pathology diagnostics. As digitization of the routine caseload advances, implementation of digital image analysis algorithms and artificial intelligence tools becomes not only attainable, but also desirable in daily sign out. The Swiss Digital Pathology Consortium (SDiPath) has initiated a Delphi process to generate best-practice recommendations for various phases of the process of digitization in pathology for the local Swiss environment, encompassing the following four topics: i) scanners, quality assurance, and validation of scans; ii) integration of scanners and systems into the pathology laboratory information system; iii) the digital workflow; and iv) digital image analysis (DIA)/artificial intelligence (AI). The current article focuses on the DIA-/AI-related recommendations generated and agreed upon by the working group and further verified by the Delphi process among the members of SDiPath. Importantly, they include the view and the currently perceived needs of practicing pathologists from multiple academic and cantonal hospitals as well as private practices

Randomized Tree Ensembles for Object Detection in Computational Pathology

Modern pathology broadly searches for biomarkers which are predictive for the survival of patients or the progression of cancer. Due to the lack of robust analysis algorithms this work is still performed manually by estimating staining on whole slides or tissue microarrays (TMA). Therefore, the design of decision support systems which can automate cancer diagnosis as well as objectify it pose a highly challenging problem for the medical imaging community. In this paper we propose Relational Detection Forests (RDF) as a novel object detection algorithm, which can be applied in an off-the-shelf manner to a large variety of tasks. The contributions of this work are twofold: (i) we describe a feature set which is able to capture shape information as well as local context. Furthermore, the feature set is guaranteed to be generally applicable due to its high flexibility. (ii) we present an ensemble learning algorithm based on randomized trees, which can cope with exceptionally high dimensional feature spaces in an efficient manner. Contrary to classical approaches, subspaces are not split based on thresholds but by learning relations between features. The algorithm is validated on tissue from 133 human clear cell renal cell carcinoma patients (ccRCC) and on murine liver samples of eight mice. On both species RDFs compared favorably to state of the art methods and approaches the detection accuracy of trained pathologists

PD-1T TILs as a Predictive Biomarker for Clinical Benefit to PD-1 Blockade in Patients with Advanced NSCLC

PURPOSE: Durable clinical benefit to PD-1 blockade in non-small cell lung cancer (NSCLC) is currently limited to a small fraction of patients, underlining the need for predictive biomarkers. We recently identified a tumor-reactive tumor-infiltrating T lymphocyte (TIL) pool, termed PD-1T TILs, with predictive potential in NSCLC. Here, we examined PD-1T TILs as biomarker in NSCLC. EXPERIMENTAL DESIGN: PD-1T TILs were digitally quantified in 120 baseline samples from advanced NSCLC patients treated with PD-1 blockade. Primary outcome was disease control (DC) at 6 months. Secondary outcomes were DC at 12 months and survival. Exploratory analyses addressed the impact of lesion-specific responses, tissue sample properties, and combination with other biomarkers on the predictive value of PD-1T TILs. RESULTS: PD-1T TILs as a biomarker reached 77% sensitivity and 67% specificity at 6 months, and 93% and 65% at 12 months, respectively. Particularly, a patient group without clinical benefit was reliably identified, indicated by a high negative predictive value (NPV) (88% at 6 months, 98% at 12 months). High PD-1T TILs related to significantly longer progression-free (HR 0.39, 95% CI, 0.24-0.63, P < 0.0001) and overall survival (HR 0.46, 95% CI, 0.28-0.76, P < 0.01). Predictive performance was increased when lesion-specific responses and samples obtained immediately before treatment were assessed. Notably, the predictive performance of PD-1T TILs was superior to PD-L1 and tertiary lymphoid structures in the same cohort. CONCLUSIONS: This study established PD-1T TILs as predictive biomarker for clinical benefit to PD-1 blockade in patients with advanced NSCLC. Most importantly, the high NPV demonstrates an accurate identification of a patient group without benefit. See related commentary by Anagnostou and Luke, p. 4835

PD-1T TILs as a Predictive Biomarker for Clinical Benefit to PD-1 Blockade in Patients with Advanced NSCLC

Purpose: Durable clinical benefit to PD-1 blockade in non- small cell lung cancer (NSCLC) is currently limited to a small fraction of patients, underlining the need for predictive biomar-kers. We recently identified a tumor-reactive tumor-infiltrating T lymphocyte (TIL) pool, termed PD-1T TILs, with predictive potential in NSCLC. Here, we examined PD-1T TILs as biomarker in NSCLC.Experimental Design: PD-1T TILs were digitally quantified in 120 baseline samples from advanced NSCLC patients treated with PD-1 blockade. Primary outcome was disease control (DC) at 6 months. Secondary outcomes were DC at 12 months and survival. Exploratory analyses addressed the impact of lesion-specific responses, tissue sample properties, and combination with other biomarkers on the predictive value of PD-1T TILs.Results: PD-1T TILs as a biomarker reached 77% sensitivity and 67% specificity at 6 months, and 93% and 65% at 12 months,respectively. Particularly, a patient group without clinical benefit was reliably identified, indicated by a high negative predictive value (NPV) (88% at 6 months, 98% at 12 months). High PD-1T TILs related to significantly longer progression-free (HR 0.39, 95% CI, 0.24-0.63, P < 0.0001) and overall survival (HR 0.46, 95% CI, 0.28-0.76, P < 0.01). Predictive performance was increased when lesion-specific responses and samples obtained immediately before treatment were assessed. Notably, the pre-dictive performance of PD-1T TILs was superior to PD-L1 and tertiary lymphoid structures in the same cohort.Conclusions: This study established PD-1T TILs as predictive biomarker for clinical benefit to PD-1 blockade in patients with advanced NSCLC. Most importantly, the high NPV demon-strates an accurate identification of a patient group without benefit

Swiss digital pathology recommendations: results from a Delphi process conducted by the Swiss Digital Pathology Consortium of the Swiss Society of Pathology.

Integration of digital pathology (DP) into clinical diagnostic workflows is increasingly receiving attention as new hardware and software become available. To facilitate the adoption of DP, the Swiss Digital Pathology Consortium (SDiPath) organized a Delphi process to produce a series of recommendations for DP integration within Swiss clinical environments. This process saw the creation of 4 working groups, focusing on the various components of a DP system (1) scanners, quality assurance and validation of scans, (2) integration of Whole Slide Image (WSI)-scanners and DP systems into the Pathology Laboratory Information System, (3) digital workflow-compliance with general quality guidelines, and (4) image analysis (IA)/artificial intelligence (AI), with topic experts for each recruited for discussion and statement generation. The work product of the Delphi process is 83 consensus statements presented here, forming the basis for "SDiPath Recommendations for Digital Pathology". They represent an up-to-date resource for national and international hospitals, researchers, device manufacturers, algorithm developers, and all supporting fields, with the intent of providing expectations and best practices to help ensure safe and efficient DP usage