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Extensive intratumoral heterogeneity (ITH) is believed to contribute to therapeutic failure and tumor recurrence, as treatment-resistant cell clones can survive and expand. However, little is known about ITH in triple-negative breast cancer (TNBC) because of the limited number of single-cell sequencing studies on TNBC. In this study, we explored ITH in TNBC by evaluating gene expression-derived and imaging-derived multi-region differences within the same tumor. We obtained tissue specimens from 10 TNBC patients and conducted RNA sequencing analysis of 2-4 regions per tumor. We developed a novel analysis framework to dissect and characterize different types of variability: between-patients (inter-tumoral heterogeneity), between-patients across regions (inter-tumoral and region heterogeneity), and within-patient, between-regions (regional intratumoral heterogeneity). We performed a Bayesian changepoint analysis to assess and classify regional variability as low (convergent) versus high (divergent) within each patient feature (TNBC and PAM50 subtypes, immune, stroma, tumor counts and tumor infiltrating lymphocytes). Gene expression signatures were categorized into three types of variability: between-patients (108 genes), between-patients across regions (183 genes), and within-patients, between-regions (778 genes). Based on the between-patient gene signature, we identified two distinct patient clusters that differed in menopausal status. Significant intratumoral divergence was observed for PAM50 classification, tumor cell counts, and tumor-infiltrating T cell abundance. Other features examined showed a representation of both divergent and convergent results. Lymph node stage was significantly associated with divergent tumors. Our results show extensive intertumoral heterogeneity and regional ITH in gene expression and image-derived features in TNBC. Our findings also raise concerns regarding gene expression based TNBC subtyping. Future studies are warranted to elucidate the role of regional heterogeneity in TNBC as a driver of treatment resistance.

The World Health Organization in its 2019 Classification of Breast Tumors termed breast sebaceous carcinoma as invasive breast carcinoma of no special type (IBC-NST), with a sebaceous pattern. Approximately 30 cases of IBC-NST with a sebaceous pattern have been reported in the literature, and in all cases the expression of mismatch repair proteins in tumors was normal. Here, we report a case of IBC-NST with a sebaceous pattern and high-frequency microsatellite instability (MSI-H). This case was a sporadic sebaceous pattern of IBC-NST with MSI-H and was unrelated to Muir-Torre syndrome. Its histopathological characteristics were similar to those of MSI-H-associated triple-negative breast carcinoma (TNBC) with a high histological grade but were without tumor-infiltrating lymphocytes (TILs). The tumor did not recur after 20 months of follow-up.

BACKGROUND: In view of continued development of new oncological approaches, there is a high demand for personalized tumor therapy. However, fast and effective functional platforms for the prediction of individual patient response to drug therapy are largely unavailable. Various promising approaches have already been described for three-dimensional cell culture models, which represent cellular complexity and almost identical structures of the original tumor tissue.
OBJECTIVE: Based on a case report, we show the capability and results of a novel test system using patient-derived microtumors (PDMs) and autologous tumor-infiltrating lymphocytes (TILs) for the prediction of response to cancer therapy.
METHODS: We established PDMs and TILs from primary tumor tissue of a renal cell carcinoma metastasis. Using immunohistochemistry and multiplex florescence-activated cell sorting (FACS ) analyses, the PDMs and TILs were characterized regarding to histology and immunophenotype. Tumor-specific cytotoxicity of standard of care and investigational compounds were assessed. The results were compared to the patient\'s individual in vivo response to therapy.
CONCLUSIONS: The cytotoxicity assay of PDMs and TILs showed a significant therapeutic response (p = 0.0004) to therapy with a programmed cell death protein 1 (PD-1) inhibitor and lenvatinib compared to the control. The in vitro results correlated positively with the in vivo data. In the future, patient-derived models could predict response to cancer therapy and may help to optimize treatment decision-making.
UNASSIGNED: HINTERGRUND: Die personalisierte Tumortherapie rückt immer mehr in den Fokus onkologischer Behandlungsstrategien. Schnelle und effektive Plattformen zur Vorhersage des individuellen Ansprechens eines Patienten auf eine medikamentöse Tumortherapie sind jedoch weitestgehend nicht verfügbar. Für dreidimensionale (3D-)Zellkulturmodelle wurden bereits verschiedene vielversprechende Ansätze beschrieben, bei denen sich Eigenschaften und Strukturen der Modelle dem des Originaltumors in hohem Maße gleichen.
UNASSIGNED: Wir zeigen anhand eines Fallberichts exemplarisch die möglichen Potenziale und Ergebnisse eines neuartigen Testsystems unter der Verwendung von patientenabgeleiteten Mikrotumoren (PDMs) und autologen tumorinfiltrierenden Lymphozyten (TILs) auf.
UNASSIGNED: Aus dem Tumorfrischgewebe einer klarzelligen Nierenzellkarzinommetastase wurden PDMs und TILs isoliert. Es wurden histologische und immunphänotypische Charakterisierungen der PDMs und TILs durchgeführt. Des Weiteren wurde funktionelle In-vitro-Substanztestungen mit typischen Medikamenten/-kombinationen durchgeführt. Die Ergebnisse wurden mit dem In-vivo-Therapieansprechen des Patienten verglichen.
UNASSIGNED: Im durchgeführten Zytotoxizitätsassay der Nierenzellkarzinommetastasen-PDMs und TILs zeigt sich ein signifikantes Therapieansprechen (p = 0,0004) auf die Therapie mit einem PD-1-Inhibitor („programmed death 1“) und Lenvatinib im Vergleich zur Kontrolle. Die erhobenen Ergebnisse in der Zellkultur korrelierten positiv mit den In-vivo-Daten, welche ebenfalls ein Ansprechen beim Patienten zeigten. Es besteht die Möglichkeit, dass patientenabgeleitete Modellsysteme ein Therapieansprechen vorhersagen könnten und daraus in Zukunft gegebenenfalls auch eine optimierte Therapieentscheidung abgeleitet werden könnte.

Although immune checkpoint blockade has demonstrated limited effectiveness against ovarian cancer, subset analyses from completed trials suggest possible superior efficacy in the clear cell carcinoma subtype.
To describe the outcomes of patients with ovarian clear cell carcinoma treated with immune checkpoint blockade.
This was a single-institution, retrospective case series of patients with ovarian clear cell carcinoma treated with a programmed cell death protein 1 (PD-1) or programmed death-ligand 1 (PD-L1) inhibitor with or without concomitant cytotoxic T-lymphocyte-associated protein 4 (CTLA-4) inhibition between January 2016 and June 2021. Demographic variables, tumor microenvironment, molecular data, and clinical outcomes were examined. Time to treatment failure was defined as the number of days between start of treatment and next line of treatment or death.
A total of 16 eligible patients were analyzed. The median treatment duration was 56 days (range 14-574); median time to treatment failure was 99 days (range 27-1568). The reason for discontinuation was disease progression in 88% of cases. Four patients (25%) experienced durable clinical benefit (time to treatment failure ≥180 days). One patient was treated twice with combined immune checkpoint blockade and experienced a complete response each time. All 12 patients who underwent clinical tumor-normal molecular profiling had microsatellite-stable disease, and all but one had low tumor mutation burden. Multiplex immunofluorescence analysis available from pre-treatment biopsies of two patients with clinical benefit demonstrated abundant tumor-infiltrating lymphocytes expressing PD-1.
Our study suggests a potential role for immune checkpoint blockade in patients with clear cell carcinoma of the ovary. Identification of genetic and microenvironmental biomarkers predictive of response will be key to guide therapy.

OBJECTIVE: Deep learning methods have demonstrated remarkable performance in pathology image analysis, but they require a large amount of annotated training data from expert pathologists. The aim of this study is to minimize the data annotation need in these analyses.
METHODS: Active learning (AL) is an iterative approach to training deep learning models. It was used in our context with a Tumor Infiltrating Lymphocytes (TIL) classification task to minimize annotation. State-of-the-art AL methods were evaluated with the TIL application and we have proposed and evaluated a more efficient and effective AL acquisition method. The proposed method uses data grouping based on imaging features and model prediction uncertainty to select meaningful training samples (image patches).
RESULTS: An experimental evaluation with a collection of cancer tissue images shows that: (i) Our approach reduces the number of patches required to attain a given AUC as compared to other approaches, and (ii) our optimization (subpooling) leads to AL execution time improvement of about 2.12×.
CONCLUSIONS: This strategy enabled TIL based deep learning analyses using smaller annotation demand. We expect this approach may be used to build other analyses in digital pathology with fewer training samples.

The combination of radiotherapy and immunotherapy improves the survival rate of patients with malignancies developed through escape from T-cell-mediated immune surveillance. Immune checkpoint inhibitors, such as anti-programmed cell death protein-ligand 1 (anti-PD-L1) antibody, are used to rescue exhausted T cells. Simultaneously, dendritic cells (DCs) which are antigen-presenting cells that can initiate T-cell activation, are used to induce a tumor-specific immune response. However, the synergistic antitumor efficacy of the aforementioned combinational immunotherapy with intratumoral injection of low-dose DCs has not been reported, and the underlying therapeutic mechanism requires further investigation. Herein, we present the special case of a psoriatic patient with cutaneous squamous cell carcinoma (cSCC) in the right inguinal region, these two diseases characterized by opposing contradiction, further complicating treatments and side-effect management efforts. To treat the intractable SCC without exaggerating psoriasis, we developed the triple-regimen therapy (TRT) with the intratumoral injection of low-dose autologous DCs and anti-PD-L1 combined with radiotherapy. The injected DCs were obtained simply through leukapheresis without prior G-CSF administration for mobilization nor tumor-antigen loading for expansion. The patient received three radiation doses (24, 18, and 18 Gy) combined with three intratumoral injections of anti-PD-L1 antibody (40, 60, and 120 mg) plus autologous DCs (80% of the DC subpopulation being CD16+ myeloid DC with approximate amounts of 7.3 × 104, 2.5 × 106, and 1.7 × 107) within 10 weeks. The efficacy of the TRT was encouraging in shrinking tumor mass with remarkable SUVmax reduction (approximately 42%) on FDG PET-Scan despite relatively low-dose DCs were available. The low-dose intratumoral immunotherapy induced mild cutaneous side effects as expected. The transcriptomes were compared between pre-TRT and post-TRT biopsies to analyze underlying mechanical pathways of the TRT protocol. Over 10 highly significantly enriched T-cell-related pathways (P <0.0001) were identified in post-TRT biopsies. In addition, the activation of both innate and adaptive immunity was significantly enriched in post-TRT peripheral blood samples. We develop the easily accessible TRT which produces both local anti-tumor T-cell responses and systemic antitumor immunity for treating cSCC patients, especially for those with autoimmune disease.

A 56-year-old male was diagnosed with right lung upper lobe squamous cancer with right hilar and mediastinum lymph node metastasis. After four cycles of neoadjuvant immunochemotherapy, reexamination by computed tomography showed progressive disease of the primary lesion. Then, the patient underwent a right lung upper lobectomy, and hilar and mediastinum lymph node dissection. Surgical pathology showed a partial response to immunochemotherapy. Single-cell RNA sequencing was used to characterize the infiltrating immune cell atlas after neoadjuvant immunochemotherapy; the most common infiltrating immune cell types were cytotoxic CD8+ T cells, monocyte-derived dendritic cells, and macrophages. Imaging mass cytometry revealed a transformation from cold to hot tumor after neoadjuvant immunochemotherapy. In this case study, we are the first to report a case of neoadjuvant immunochemotherapy pseudoprogression, proved by surgical pathology, single-cell RNA sequencing, and imaging mass cytometry. Both single-cell RNA sequencing and imaging mass cytometry revealed an activated immune microenvironment after neoadjuvant immunochemotherapy.

Immune checkpoint inhibitors have revolutionized the treatments of lung cancers, and multiple predictive biomarkers alone or in combination help clinicians with the appropriate therapeutic selections. Recently, chemo-immunotherapy has been recommended for treating advanced non-small cell lung cancers in patients without driver mutations. However, the clinical relevance of predictive biomarkers and the treatment efficacy of chemo-immunotherapy in large cell lung carcinoma (LCLC) remain unclear. Here, we reported a rare case of LCLC with none driver gene mutations and low values of multiple predictive biomarkers. These biomarkers included a low PD-L1 expression of 5-10%, a low tumor mutational burden (TMB) of 2.5 muts/mb, a low CD8(+) tumor-infiltrating lymphocyte density of 147.91 psc/mm². After one-cycle chemotherapy, the patient progressed rapidly and then was switched to pembrolizumab combining paclitaxel plus cisplatin. Interestingly, he achieved a partial response after two cycles of chemo-immunotherapy, showing multiple lymph nodes obviously shrunk on CT scan, and other clinical symptoms were relieved when compared with the baseline findings. After five cycles of chemo-immunotherapy, this advanced patient still benefited and was changed to maintenance immunotherapy monotherapy. This case suggests that chemo-immunotherapy may provide an effective therapeutic option for those LCLC patients with low values of multiple predictive biomarkers, particularly for those who progressed from first-line classical treatments.