Molecular Profiling of Tumors

Slide 1 · Molecular Profiling of Tumors

Integrating tumor genomes, expression profiles and histopathology

From sequencing readouts to clinically meaningful interpretation

Dr Abu Suraih Sakhri

MBBS, MD (Pathology)

www.abusuraihsakhri.com

Slide 2 · Learning outcomes

Map molecular layers

Distinguish genome, epigenome, transcriptome, proteome and metabolome readouts.

Interpret a profile

Explain targeted sequencing, coverage, alteration classes and signatures.

Connect to care

Relate tumor profiles to target selection, therapy response and predictive assays.

Integrate morphology

Show why histology, microenvironment and molecular data remain complementary.

Slide 3 · The central diagnostic problem

Morphology asks

Molecular profiling asks

What kind of tumor is present?

How invasive, heterogeneous and differentiated is it?

What is the tissue and stromal context?

Which genetic or expression alterations occur?

Are actionable targets or pathway defects detectable?

Do molecular patterns inform treatment selection?

Neither information stream is a complete substitute for the other.

Slide 4 · From one gene to a molecular profile

Traditional

Investigate a selected gene or lesion.

→

High-throughput

Measure many alterations at one time.

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Interpretation

Resolve molecular patterns, not just isolated findings.

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Integration

Combine profile with the tumor diagnosis.

The conceptual shift is breadth of measurement and integrated interpretation.

Slide 5 · The “omics” landscape

Genome

DNA sequence and genomic alterations.

Epigenome

Genome-wide regulatory modifications.

Transcriptome

RNAs expressed by the cell population.

Proteome

Many expressed proteins measured together.

Metabolome

A snapshot of cellular metabolites.

Slide 6 · Genomics measures altered DNA

High-throughput sequencing surveys genomic lesions across multiple genes.

Cancer genomes harbor sequence and structural changes of different types.

A DNA profile supplies potential diagnostic and therapeutic information.

Genomic measurement alone does not describe cell architecture or local immunity.

Interpretation

DNA profiles characterize alterations; They do not replace tissue interpretation.

Slide 7 · The epigenome is a distinct layer

DNA sequence

Base order within the genome.

→

Epigenetic state

Chemical regulatory pattern, including methylation.

→

Epigenetic fingerprint

Abnormal methylation pattern associated with cancer.

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Classification

Use this fingerprint as one line of molecular evidence.

Epigenetic classification is not interchangeable with sequence mutation detection.

Slide 8 · Transcriptomics measures expressed RNA

RNA abundance provides a readout of expressed genes.

RNA profiling and DNA sequencing answer different biological questions.

RNA-based assays can identify expressed gene-fusion transcripts.

Expression patterns can assist estimates of treatment benefit in selected tumors.

Interpretation

A DNA change identifies a lesion; An RNA assay assesses expressed transcripts.

Slide 9 · Proteomics measures tumor proteins

Readout

Measures many proteins simultaneously.

Biological layer

Represents products of gene expression.

Interpretation

Protein-level patterns are distinct from DNA sequence changes.

Proteomics among global molecular approaches; This passage does not specify a clinical assay.

Slide 10 · Metabolomics measures biochemical state

Readout

Collective snapshot of cellular metabolites.

Biological layer

Reflects downstream cellular chemical activity.

Interpretation

Describes a different axis than genomic and transcriptomic profiles.

No tumor-specific metabolomic cutoff or treatment test is established in the assigned passage.

Slide 11 · Different omics, different questions

Layer

What is measured?

Illustrative question

Genomics

DNA variants

Which alterations are present?

Epigenomics

Regulatory modifications

What methylation pattern exists?

Transcriptomics

RNA abundance / transcripts

What is expressed?

Proteomics

Proteins

What proteins are present?

Metabolomics

Metabolites

What chemical state is captured?

Slide 12 · Why DNA profiling entered pathology first

Sequencing as the most clinically influential omics platform.

DNA can be recovered from formalin-fixed, paraffin-embedded tissue.

Such tissue blocks are routinely retained by pathology departments.

This specimen compatibility accelerated clinical use of high-throughput assays.

Interpretation

Routine archival tumor material made sequencing operationally practical.

Slide 13 · FFPE tissue links histology and genomics

Tumor specimen

Pathologist establishes morphologic context.

→

FFPE block

Retained tissue provides recoverable DNA.

→

DNA sequencing

Tumor-derived alterations are interrogated.

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Joint report

Molecular findings interpreted with tumor type.

Schematic clinical pathway; Not an assay validation diagram.

Slide 14 · A profile is derived from sampled tumor

The molecular sample

The tissue section

Provides DNA or RNA from sampled material.

Reflects the portion of tumor actually analyzed.

May reveal multiple genomic alterations.

Reveals invasive architecture and anaplasia.

Shows within-tumor heterogeneity.

Retains stromal and immune context.

Sampling and morphology shape how a profile should be understood.

Slide 15 · TCGA: Systematic cancer genome cataloging

The Cancer Genome Atlas (TCGA) is a major NCI-sponsored consortium.

Large-scale studies systematically cataloged genetic alterations across cancer types.

The resulting complexity motivated integrative genomic displays.

Research cancer catalogs and an individual clinical report serve different purposes.

Interpretation

A genomic atlas maps patterns; A clinical report interprets a patient’s tumor.

Slide 16 · Cancer genomes contain multiple lesion classes

Sequence variants

Point mutations and other changes in nucleotide sequence.

Copy-number changes

Regions of increased or decreased genetic material.

Allelic imbalance

Unequal representation of alleles, including loss of heterozygosity.

Rearrangements

Structural changes within or between chromosomes.

Slide 17 · Circos plots: An integrative view

Schematic rings, not patient data

Chromosome ring

Genomic locations on a circular axis

Rearrangement links

Within-/between-chromosome events

Track layers

Allelic imbalance and copy-number changes

Mutation marks

Point mutation positions

Diagrammatic representation of the tracks Fig. 7.48

Slide 18 · Structural rearrangements in profiles

Intrachromosomal

Interchromosomal

Structural change within one chromosome.

In the referenced circos plot, represented with blue links.

Rearrangement involving different chromosomes.

In the referenced plot, represented with red links.

Link location matters: These categories are not point mutations.

Slide 19 · Loss of heterozygosity and allelic balance

Two alleles

Reference state includes contributions from two alleles.

→

Allelic imbalance

One allele becomes over-represented relative to the other.

→

Profile track

Displays imbalance and regions of LOH.

The caption of Fig. 7.48 displays LOH/imbalance as a separate track.

Slide 20 · Copy-number gain versus loss

Copy-number gain

Copy-number loss

Increased genomic material in a region.

Shown as gain on a copy-number track.

May occur alongside other alteration classes.

Reduced genomic material in a region.

Shown as loss on a copy-number track.

Is distinct from a sequence point mutation.

Gain and loss are relative copy-number categories, not invented counts.

Slide 21 · Point mutations are another profile layer

Point mutations alter the DNA nucleotide sequence at a position.

In Fig. 7.48, point mutations are represented by dots on a genomic track.

A point mutation may be a driver or a passenger.

Interpretation depends on the precise alteration and tumor context.

Interpretation

The presence of a dot is not equivalent to an actionable result.

Slide 22 · Reading a multi-layer genome view

Track / link

Encoded information

Interpretation

Chromosome ring

Genomic coordinate

Where the event lies

Structural links

Within/between chromosomes

How regions are rearranged

Allelic track

LOH / imbalance

Relationship of alleles

Copy-number track

Gain / loss

Relative regional DNA dosage

Mutation marks

Sequence alterations

Sites of point mutation

Slide 23 · Next-generation sequencing in cancer

NGS permits many genes or regions to be sequenced in parallel.

The assigned text describes routine targeted testing in cancer centers and reference laboratories.

Clinical panels apply to both solid and hematologic malignancies.

A profile is interpreted to answer a clinical question, not merely to list variants.

Interpretation

A broad sequencing readout requires tumor-specific clinical interpretation.

Slide 24 · Targeted sequencing narrows the search

Select targets

Genes, exons or recurrent mutation hotspots.

→

Sequence deeply

Obtain repeated read coverage over regions.

→

Call alterations

Identify detectable sequence changes.

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Review findings

Assess clinical relevance with diagnosis.

Targeted sequencing maximizes attention on selected cancer-related regions.

Slide 25 · Panels and hotspots are not identical

Hotspot strategy

Multi-gene panel

Emphasizes recurrently mutated positions.

Provides focused analysis of predefined loci.

Does not interrogate unselected regions.

Can cover exons from hundreds of genes.

Expands the range of alteration opportunities.

Still remains bounded by assay design.

The profile cannot establish absence of abnormalities outside its designed targets.

Slide 26 · Sequencing depth is fold coverage

Depth denotes how many sequence reads cover the region under interrogation.

Greater depth can support detection of variants present in minority populations.

Panels designed to detect mutations in small tumor subpopulations.

Analytical sensitivity depends on more than the number of targets on a panel.

Interpretation

Depth describes read coverage, not the fraction of tumor in a tissue section.

Slide 27 · Minor variants and tumor heterogeneity

Mixed tumor

Not every sampled neoplastic cell need carry each variant.

→

Deep coverage

Provides repeated evidence at the interrogated locus.

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Minority variant

May be detected if within assay sensitivity.

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Clinical meaning

Requires context beyond the read count.

Schematic illustration of why sequencing depth matters; No simulated frequencies.

Slide 28 · What a negative panel cannot prove

Limited regions

An alteration outside the targeted region may not be assessed.

Detection limit

A low-abundance variant may fall below sensitivity.

Interpretation

A negative targeted panel is not a tumor-wide declaration of genetic normality.

These limitations follow from targeted coverage and the role of sequencing depth.

Slide 29 · Actionability: The aim of many panels

Find lesion

Identify a genomic alteration.

→

Determine effect

Consider oncogenic relevance.

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Link a target

Identify possible therapy-directed relevance.

→

Use context

Integrate tumor type and other pathology.

A genomic lesion is not automatically a treatment indication.

Slide 30 · A driver is not every observed mutation

Driver alteration

Passenger alteration

Contributes to malignant cell growth or survival.

May be therapeutically relevant in a defined tumor.

Accompanies tumor development without acting as the principal driver.

Can still contribute to the overall mutation pattern.

Both types can be visible in a tumor-wide sequence profile.

Slide 31 · Lung carcinoma illustrates molecular diversity

Lung carcinomas comprise genetically diverse neoplasms.

A common histologic label does not imply a single targetable alteration.

Molecular typing is especially relevant when selecting a targeted strategy.

Tissue diagnosis remains essential for interpreting which findings matter.

Interpretation

For genetically diverse tumors, classification and treatment selection need different kinds of information.

Slide 32 · Examples of treatment-relevant lesions

Tumor setting

Illustrative lesion

Clinical use described

Lung cancer

EGFR alteration

Guide oncoprotein-directed treatment

Lung cancer

ALK rearrangement

Identify a therapeutically relevant driver

Melanoma

BRAF mutation

Identify potential BRAF-directed treatment

CML

BCR–ABL fusion

Molecular diagnosis and treatment guidance

Slide 33 · Clinical interpretation is not gene matching

Molecular evidence

Pathology evidence

Exact lesion is identified.

Biologic or therapeutic mechanism is considered.

A putative target is recognized.

Tumor lineage and subtype are established.

Morphology documents invasion and differentiation.

Tissue-specific treatment behavior is relevant.

This is why a target list is not a complete cancer diagnosis.

Slide 34 · Mutation patterns add information

Many mutations

Tumor has an aggregate pattern of alterations.

→

Signature

Their distribution suggests an underlying process.

→

Repair defect

A characteristic signature may point to a mechanism.

→

Clinical linkage

Selected patterns inform therapy decisions.

Patterns may use passenger as well as driver mutations.

Slide 35 · Microsatellite instability and MMR

Mismatch repair (MMR) defects produce a characteristic abnormal mutation phenotype.

Microsatellite instability (MSI) in this setting.

The assay finding can carry clinical meaning beyond a single driver gene.

MSI is a molecular phenotype; It should not be equated with any isolated mutation.

Interpretation

Interpret the defect or phenotype, not a solitary mutation name.

Slide 36 · MMR deficiency can redirect therapy

Tumor profiled

Assess relevant repair-defect features.

→

MMR-deficient pattern

Defect or associated phenotype established.

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Checkpoint strategy

A tumor-agnostic therapeutic rationale may apply.

→

Integrated review

Check clinical setting and final pathology.

Checkpoint blockade based on MMR defects across tumor types.

Slide 37 · Tumor-agnostic is a qualified concept

Histology-based decision

Molecularly defined decision

Start with anatomic site and tumor subtype.

Integrate stage, morphology and lineage.

A shared molecular defect may support a cross-tumor therapeutic approach.

Example: MMR deficiency and checkpoint blockade.

A molecular indication does not erase the diagnosis or other clinical requirements.

Slide 38 · RNA presents technical challenges

RNA is more vulnerable to degradation than DNA.

Fixed tissue can still yield clinically useful RNA readouts.

The RNA analyte and assay must be suited to the intended question.

Continuing platform development to improve RNA measurement.

Interpretation

DNA and RNA workflows cannot be treated as analytically interchangeable.

Slide 39 · Fusion transcripts are RNA targets

Genomic fusion

Genetic event joins regions from different genes.

→

Fusion transcript

RNA can contain the resulting joined sequence.

→

RNA assay

Interrogate the expressed fusion sequence.

→

Interpretation

Use the finding with tumor identity and morphology.

RNA sequencing and RNA-based assays for fusion detection.

Slide 40 · Anchored RT-PCR adds diagnostic reach

Anchored reverse-transcriptase PCR characterizes tumor-derived fusion transcripts.

The method characterizes fusion transcripts from fixed tumor tissue.

The approach broadens the ability to recognize tumor-specific RNA rearrangements.

Fusion evidence assists classification when morphology alone is insufficient.

Interpretation

Fusion-transcript analysis can complement morphology when several tumor entities appear similar.

Slide 41 · Breast cancer uses expression profiling

Breast-cancer RNA-based tests are described as routine in the source.

Expression profiling can help assess whether chemotherapy is likely to benefit a patient.

The clinical question is treatment benefit, rather than simply tumor detection.

This is an example of predictive use of a multi-gene molecular readout.

Interpretation

Specific assay thresholds and eligibility criteria are not supplied by this excerpt.

Slide 42 · Diagnostic, prognostic, predictive

Purpose

Clinical question

Profile-related example

Diagnostic

What neoplasm is present?

Molecularly defined subtype

Prognostic

What clinical course is expected?

Genomic alterations linked to outcome

Predictive

Will a therapy likely help?

Breast expression profile / actionable lesion

Slide 43 · Classification by molecule or morphology?

Original paradigm

Classify by tissue and morphologic differentiation.

→

Molecular expansion

Recognize shared mutations across tumor types.

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Temptation

Group cancers by a target alone.

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Reconciliation

Use both tumor identity and profile.

Figure 7.49 contrasts tissue-origin and target-centered approaches.

Slide 44 · Shared mutation, distinct malignancies

Hairy cell leukemia

One BRAF-mutated tumor setting in the source.

Melanoma

A morphologically different BRAF-mutated cancer.

Colorectal carcinoma

BRAF lesions may also occur in colorectal carcinoma.

Other tumor examples

Papillary thyroid carcinoma and Langerhans cell histiocytosis also appear in Fig. 7.50.

Slide 45 · BRAF links genotype to MAPK signaling

RAS pathway

Upstream signaling network.

→

BRAF kinase

Serine/threonine kinase in this pathway.

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Gain-of-function

Activating BRAF alteration changes signaling.

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Therapy question

Could BRAF inhibition alter tumor behavior?

Original schematic reflects only the pathway relation stated in the source.

Slide 46 · BRAF inhibition is context dependent

BRAF-mutated tumor

Response pattern described

Teaching implication

Hairy cell leukemia

Typically sustained response

Genotype may be highly informative

Melanoma

Often transient response

Response may not endure

Colorectal carcinoma

Limited response in source account

Genotype alone is insufficient

Qualitative examples reproduced; Not modern regimen-specific efficacy estimates.

Slide 47 · The molecular target is not the whole tumor

A shared BRAF alteration does not yield identical responses in every tissue.

The source notes that the reasons for divergent inhibitor responses are uncertain.

Histologic identity retains predictive and diagnostic context.

Therapy selection therefore requires more than a mutation-matched drug label.

Interpretation

Molecular similarity must not be mistaken for clinical equivalence.

Slide 48 · Anaplasia is visible in tissue

Histopathology evaluates tumor differentiation and anaplasia.

These properties are represented spatially in actual tissue architecture.

A sequence-based gene list does not directly show the degree of anaplasia.

Combined reports preserve both phenotypic and genomic information.

Interpretation

Morphologic interpretation contributes information a DNA readout does not encode directly.

Slide 49 · Invasion is a morphologic relationship

Tumor cells

Identify a neoplastic population.

→

Tissue boundary

Review its relationship to surrounding structures.

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Invasion

Determine evidence of infiltrative growth.

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Clinical context

Integrate this property with molecular findings.

This is a conceptual analytic workflow, not a depiction of a histology specimen.

Slide 50 · Tumor heterogeneity spans multiple scales

Molecular

Different alterations may be present within tumor populations.

Histologic

Morphology may vary across regions of the same tumor.

Microenvironmental

Stroma, vessels and immune cells differ by region.

Spatial context explains why a bulk profile and a section view are complementary.

Slide 51 · Stroma and angiogenesis matter

Tumors interact with surrounding non-neoplastic stromal elements.

Angiogenesis is one tissue-level property visible in the tumor environment.

Molecular profiling alone does not display vascular architecture.

Pathology interpretation links neoplastic cells with the supporting tissue.

Interpretation

The tumor is a tissue system, not merely a list of somatic variants.

Slide 52 · Host immunity is part of the phenotype

Host immune responses can be evaluated in relation to the tumor.

Immune evasion has therapeutic relevance.

Global tumor molecular data and tissue immunity interrogate different domains.

Spatially resolved tools aim to connect expression with cell and tissue location.

Interpretation

The immediate immune context may influence interpretation of therapies.

Slide 53 · Spatial transcriptomics adds location

Illustrative tissue map

T

T

T

S

S

T

T

I

S

S

T

T

I

S

S

T: Tumor S: Stromal I: Immune

Conventional bulk RNA

Expression across the sampled population

Spatial RNA profiling

Expression associated with tissue position

Integrated reading

Relate tumor, stroma and immune-cell locations

Schematic only — no observed cells or expression measurements

Slide 54 · Bulk and spatial RNA readouts differ

Bulk transcriptome

Spatial transcriptomics

Aggregates RNA from a sampled population.

Characterizes overall expression patterns.

Cannot independently recover full tissue topology.

Couples expression to mapped tissue locations.

Can explore tumor–stroma and immune relationships.

Still requires morphologic interpretation.

These approaches differ chiefly in whether spatial position is retained.

Slide 55 · DNA methylation is a tumor fingerprint

Cancer-associated state

Abnormal methylation patterns emerge.

→

Profile measurement

Assess methylation across selected sites.

→

Molecular fingerprint

Capture a characteristic epigenetic pattern.

→

Clinical use

Aid classification and potentially response prediction.

Methylation changes are epigenetic, not alterations of the DNA base sequence.

Slide 56 · Methylation profiling has a different role

DNA sequencing

Methylation profiling

Interrogates sequence alterations.

Can reveal putative driver targets.

Uses mutation and genome structure readouts.

Measures epigenetic signatures.

Can assist tumor classification.

Source also notes potential prediction of therapy response.

The two methods can contribute complementary molecular classifications.

Slide 57 · How to interpret a multi-omic profile

Ask the question

Diagnosis, prognosis or therapy prediction?

→

Select assay

DNA, RNA, methylation or another layer.

→

Read result

Alterations, signatures or expression pattern.

→

Integrate

Interpret with morphology and setting.

The best assay depends on what the clinician and pathologist need to know.

Slide 58 · When a result is biologically informative

Detected

The method identifies a particular measured alteration.

Interpreted

Its relevance is considered in the correct neoplastic context.

Clinically used

The result informs diagnosis, outcome assessment or treatment discussion.

A detected alteration is not automatically a validated treatment decision.

Slide 59 · Integrated pathology report: Two axes

Histopathology supplies

Molecular testing supplies

Joint interpretation

Lineage and tumor subtype

Alterations and molecular class

Refined diagnosis

Anaplasia and invasion

Relevant genetic signatures

Biologic risk context

Stroma and immune context

RNA / epigenetic readouts

Potential therapy implications

Slide 60 · Clinical exercise: Diverse lung carcinoma

Case prompt

A lung carcinoma has been diagnosed histologically. A clinician asks whether targeted therapy is plausible.

Reasoning

The Robbins text emphasizes genetic diversity within lung carcinomas.

An NGS panel may detect a relevant driver in an eligible tumor.

The final result must retain histologic and clinical context.

Molecular profiling complements—not replaces—the established tumor diagnosis.

Slide 61 · Clinical exercise: BRAF across tissues

Case prompt

BRAF gain-of-function alterations are identified in three tumors of different histologic types.

Reasoning

Heterogeneous responses to BRAF inhibitors.

Hairy cell leukemia, melanoma and colon carcinoma behave differently.

A shared target is not sufficient to assume the same benefit.

Report the precise genomic finding together with the histologic identity.

Slide 62 · Clinical exercise: Repair-defect signature

Case prompt

A tumor has a molecular pattern associated with mismatch repair deficiency.

Reasoning

The genome-wide mutation pattern provides information beyond a single driver.

MMR defects with checkpoint-blockade decisions.

Therapeutic relevance can extend across anatomical tumor categories.

Recognize the repair-defect phenotype and its potential tumor-agnostic significance.

Slide 63 · Clinical exercise: A breast RNA profile

Case prompt

The treatment team requests an expression-based assessment of likely chemotherapy benefit in breast cancer.

Reasoning

The task is predictive rather than merely diagnostic.

RNA profiles capture gene-expression information.

The excerpt provides no cutoff or specific assay eligibility rule.

Explain the intended clinical use while avoiding unsupported numerical thresholds.

Slide 64 · Synthesis: Molecular plus morphologic

Find the lesion

Sequencing characterizes alterations and signatures.

Read the phenotype

Histopathology identifies type, invasion and anaplasia.

Understand the context

Tumor, stroma and immunity shape biological behavior.

Make a reasoned decision

A target is useful only after integrated clinical interpretation.

Slide 65 · Source and reading

Robbins, Cotran & Kumar. Pathologic Basis of Disease. 11th ed. Chapter 7, “Molecular Profiling of Tumors,” printed pp. 298–300.

Figures 7.48–7.50: referenced for their documented concepts; original figures are not reproduced.

Adjacent context: “Molecular Diagnostics and Cytogenetics,” printed pp. 297–298.

Examples and schematics are educational reorganizations of these assigned passages.